diff --git a/09-29/eigenfaces/cameraman.tif b/09-29/eigenfaces/cameraman.tif new file mode 100755 index 0000000..bf8495b Binary files /dev/null and b/09-29/eigenfaces/cameraman.tif differ diff --git a/09-29/eigenfaces/eigenfaces_classify.m b/09-29/eigenfaces/eigenfaces_classify.m new file mode 100755 index 0000000..2122981 --- /dev/null +++ b/09-29/eigenfaces/eigenfaces_classify.m @@ -0,0 +1,38 @@ +function [matched_individual,bestmatchdistance]=eigenfaces_classify(test,training,n); +%classifies using n principal components, closest match +[w, h, nExpressions, nIndividuals]=size(training); +X=reshape(training,[w*h,nIndividuals*nExpressions]); +avg=mean(X,2); +Xd=bsxfun(@minus,X,avg); +[U,S,V]=svd(Xd,0); +Xt=reshape(test,w*h,numel(test)/(w*h)); +Xtd=bsxfun(@minus,Xt,avg); +scores=U(:,1:n)'*Xtd; +trainingscores=U(:,1:n)'*Xd; +%normalize scores and samples +%scores=bsxfun(@rdivide,scores,sqrt(sum(abs(scores).^2))); +%trainingscores=bsxfun(@rdivide,trainingscores,sqrt(sum(abs(trainingscores).^2))); +%cosine similarity +%C=scores'*trainingscores; +%[bestmatchdistance bestmatchindex]=max(C,[],2); +%matched_individual=ceil(bestmatchindex/nExpressions); + +%Euclidean distance +distanceMatrix=nan(size(scores,2),size(trainingscores,2)); +for i=1:size(scores,2) + for j=1:size(trainingscores,2) + distanceMatrix(i,j)=norm(scores(:,i)-trainingscores(:,j)); + end +end +[bestmatchdistance bestmatchindex]=min(distanceMatrix,[],2); +matched_individual=ceil(bestmatchindex/nExpressions); +if numel(test)==w*h + subplot(1,2,1); + imagesc(test); + colormap(gray); + subplot(1,2,2); + imagesc(reshape(X(:,bestmatchindex),[w,h])); + colormap(gray); + disp('best match distance='); + disp(bestmatchdistance); +end \ No newline at end of file diff --git a/09-29/eigenfaces/eigenfaces_scatter.m b/09-29/eigenfaces/eigenfaces_scatter.m new file mode 100755 index 0000000..4614f12 --- /dev/null +++ b/09-29/eigenfaces/eigenfaces_scatter.m @@ -0,0 +1,18 @@ +function eigenfaces_scatter(images, indices); + +[w, h, nExpressions, nIndividuals]=size(images); +X=reshape(images,[w*h,nIndividuals*nExpressions]); +avg=mean(X,2); +Xd=bsxfun(@minus,X,avg); +[U,S,V]=svd(Xd,0); +scores=U(:,indices)'*Xd; +%normalize scores and samples +%scores=bsxfun(@rdivide,scores,sqrt(sum(abs(scores).^2))); + +if length(indices) == 3 + scatter3(scores(1,:),scores(2,:),scores(3,:),50*ones(size(scores(1,:))),kron(1:nIndividuals,ones(1,nExpressions))); +elseif length(indices) == 2 + scatter(scores(1,:),scores(2,:),50*ones(size(scores(1,:))),kron(1:nIndividuals,ones(1,nExpressions))); +else + error('wrong indices size'); +end diff --git a/09-29/eigenfaces/interactiverec.m b/09-29/eigenfaces/interactiverec.m new file mode 100755 index 0000000..c8fdf2b --- /dev/null +++ b/09-29/eigenfaces/interactiverec.m @@ -0,0 +1,29 @@ +function interactiverec(F) +% given a 243x320 image F, displays it as sum of components +stdsize=[243,320]; + +F = F(:); + +if not(numel(F) == prod(stdsize)) + error('The first argument must be the picture to reconstruct'); +end + +X=readyalefaces_to_tensor; +X=reshape(X,[prod(stdsize),numel(X)/prod(stdsize)]); +avg=mean(X,2); +Xs=bsxfun(@minus,X,avg); +[U,S,V]=svd(X,0); +colormap(gray); +ncolors=size(gray,1); +h = image(reshape(avg,stdsize)*ncolors); + +% Add a slider +uicontrol('Style', 'slider', 'Min', 0, 'Max', size(U,2), ... + 'Callback', @callback,'Position',[10 0 300 20]); + +function callback(src,evt) +d=round(get(src, 'Value')) +set(h, 'CData', reshape(ncolors*(avg+U(:,1:d)*(U(:,1:d)'*F)),stdsize)); +end + +end diff --git a/09-29/eigenfaces/otherfaces/bart.png b/09-29/eigenfaces/otherfaces/bart.png new file mode 100755 index 0000000..b8e5c50 Binary files /dev/null and b/09-29/eigenfaces/otherfaces/bart.png differ diff --git a/09-29/eigenfaces/otherfaces/bart.xcf b/09-29/eigenfaces/otherfaces/bart.xcf new file mode 100755 index 0000000..b0818dc Binary files /dev/null and b/09-29/eigenfaces/otherfaces/bart.xcf differ diff --git a/09-29/eigenfaces/otherfaces/bart_unedited.jpg b/09-29/eigenfaces/otherfaces/bart_unedited.jpg new file mode 100755 index 0000000..096d02b Binary files /dev/null and b/09-29/eigenfaces/otherfaces/bart_unedited.jpg differ diff --git a/09-29/eigenfaces/otherfaces/car.png b/09-29/eigenfaces/otherfaces/car.png new file mode 100755 index 0000000..5146145 Binary files /dev/null and b/09-29/eigenfaces/otherfaces/car.png differ diff --git a/09-29/eigenfaces/otherfaces/car.xcf b/09-29/eigenfaces/otherfaces/car.xcf new file mode 100755 index 0000000..45750a3 Binary files /dev/null and b/09-29/eigenfaces/otherfaces/car.xcf differ diff --git a/09-29/eigenfaces/otherfaces/car_unedited.jpg b/09-29/eigenfaces/otherfaces/car_unedited.jpg new file mode 100755 index 0000000..8515d9c Binary files /dev/null and b/09-29/eigenfaces/otherfaces/car_unedited.jpg differ diff --git a/09-29/eigenfaces/readyalefaces_to_tensor.m b/09-29/eigenfaces/readyalefaces_to_tensor.m new file mode 100755 index 0000000..162edb1 --- /dev/null +++ b/09-29/eigenfaces/readyalefaces_to_tensor.m @@ -0,0 +1,34 @@ +function [F,descr] = readyalefaces_to_tensor(str) + +if not(exist('str','var')) + str='all'; +end +switch str + case 'easy' + extensions = {'happy', 'normal', 'sad', 'sleepy', 'surprised', 'wink' }; + case 'easy-nowink' + extensions = {'happy', 'normal', 'sad', 'sleepy', 'surprised' }; + case 'nowink' + extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised' }; + case {'hard','all'} + extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' }; + otherwise + error 'unknown selector'; +end + +for i = 1 : 15, + basename = 'yalefaces/subject'; + if( i < 10 ) + basename = [basename, '0', num2str(i)]; + else + basename = [basename, num2str(i)]; + end; + + for j = 1:length(extensions), + fullname = [basename, '.', extensions{j}, '.gif']; + X = imread(fullname); + F(:,:,j,i) = double(X)/255; + end; + +end; +descr=extensions; \ No newline at end of file diff --git a/09-29/eigenfaces/showyalefaces.m b/09-29/eigenfaces/showyalefaces.m new file mode 100755 index 0000000..59c99b9 --- /dev/null +++ b/09-29/eigenfaces/showyalefaces.m @@ -0,0 +1,13 @@ +X=readyalefaces_to_tensor; +%X = X(:,:,1:4,[12,9,6,1]); %filters out only some faces +[w h expr ind]=size(X); +clf; +for i=1:expr + for j=1:ind + subplot('position', [(i-1)/expr, (j-1)/ind,1/expr,1/ind]); +% subplot(expr,ind,j+(i-1)*ind); + imagesc(X(:,:,i,j)); + colormap(gray); + end +end +set(findobj(gcf, 'type','axes'), 'Visible','off') \ No newline at end of file diff --git a/09-29/eigenfaces/tightsubplot.m b/09-29/eigenfaces/tightsubplot.m new file mode 100755 index 0000000..8d6b09f --- /dev/null +++ b/09-29/eigenfaces/tightsubplot.m @@ -0,0 +1,7 @@ +function tightsubplot(dim, i, data) + +row = mod(i-1, dim); +col = floor((i-1) / dim); +subplot('position', [row*(1/dim), (dim-col-1)*(1/dim), 1/dim-.001, 1/dim-0.001 ]); +imagesc(data); +axis off; \ No newline at end of file diff --git a/09-29/eigenfaces/yalefaces/README b/09-29/eigenfaces/yalefaces/README new file mode 100755 index 0000000..f8029c0 --- /dev/null +++ b/09-29/eigenfaces/yalefaces/README @@ -0,0 +1,29 @@ + The Yale Face Database + ---------------------- + +The database contains 165 GIF images of 15 subjects (subject01, +subject02, etc.). There are 11 images per subject, one for each +of the following facial expressions or configurations: center-light, +w/glasses, happy, left-light, w/no glasses, normal, right-light, +sad, sleepy, surprised, and wink. Note that the image "subject04.sad" +has been corrupted and has been substituted by "subject04.normal". + +All the images including this readme file are contained in the file +"yalefaces.tar". It can be unpacked with: + + tar xvf yalefaces.tar + +A directory called "yalefaces" will be created containing all the images. +These can be viewed using "xv" or ported with any software package that +can understand the GIF format. + +You are free to use the Yale Face Database for research purposes. +If experimental results are obtained that use images from within the +database, all publications of these results should acknowledge the use +of the "Yale Face Database." Without permission from Yale, images +from within the database cannot be incorporated into a larger database +which is then publicly distributed. + +If you have any trouble or questions please email Prof. David Kriegman +(kriegman@yale.edu) or Prof. Peter Belhumeur (Belhumeur@ledoux.eng.yale.edu). + diff --git a/09-29/eigenfaces/yalefaces/subject01.centerlight.gif b/09-29/eigenfaces/yalefaces/subject01.centerlight.gif new file mode 100755 index 0000000..fb48579 Binary files /dev/null and b/09-29/eigenfaces/yalefaces/subject01.centerlight.gif differ diff --git a/09-29/eigenfaces/yalefaces/subject01.glasses.gif b/09-29/eigenfaces/yalefaces/subject01.glasses.gif new file mode 100755 index 0000000..8832067 Binary files /dev/null and b/09-29/eigenfaces/yalefaces/subject01.glasses.gif differ diff --git a/09-29/eigenfaces/yalefaces/subject01.happy.gif b/09-29/eigenfaces/yalefaces/subject01.happy.gif new file mode 100755 index 0000000..358ba87 Binary files /dev/null and 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[ + "using DelimitedFiles\n", + "# https://docs.julialang.org/en/v1/stdlib/DelimitedFiles/\n", + "\n", + "M = convert(Matrix{Int}, readdlm(\"salaries.csv\", ',', skipstart=1)[:, 2:end]);\n", + "\n", + "A = M[1:end, 1:3];\n", + "y = M[1:end, 4];\n", + "x = A \\ y;\n", + "\n", + "println(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e7b91a49-8da1-4637-9336-d33e089f82c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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https://docs.juliaplots.org/latest/tutorial/\n", + "\n", + "plot(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fafdd151-c5c4-494e-9793-60f45f49baff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4-element Vector{Float64}:\n", + " 0.020055886001898972\n", + " 8.201817529871014e-5\n", + " -1.001646828763499\n", + " 1.5016847772666466" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = hcat(x.^3, x.^2, x, ones(1000));\n", + "coefficients = A \\ y" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c01d514a-c7c0-4371-ac18-35cbed47d6c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"id": "427bb23c-0435-4c9c-8d45-6e912850c6d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-element Vector{Float64}:\n", + " -1.3463569041275599e-15\n", + " 1.6972243622680054\n", + " 5.302775637731994" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = [2 0 1;0 1 1; 0 0 0; 0 0 0];\n", + "using LinearAlgebra # from stdlib\n", + "\n", + "eigvals(A' * A)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/09-29/salaries.csv b/09-29/salaries.csv new file mode 100644 index 0000000..a67e624 --- /dev/null +++ b/09-29/salaries.csv @@ -0,0 +1,399 @@ +Name,Rebounds,Fouls,Points,Salary +Aaron Brooks,101,132,491,2700000 +Aaron Gordon,507,153,719,4351320 +Aaron Harrison,15,10,18,874636 +Adreian Payne,111,77,132,2022240 +Al Horford,596,163,1249,26540100 +Al Jefferson,301,117,562,10230179 +Al-Farouq Aminu,499,171,839,7680965 +Alan Anderson,27,25,65,1315448 +Alan Williams,38,15,29,874636 +Alec Burks,109,71,412,10154495 +Alex Len,594,230,703,4823621 +Alexis Ajinca,269,134,352,4600000 +Allen Crabbe,216,192,832,18500000 +Alonzo Gee,245,169,325,1400000 +Amir Johnson,505,214,577,12000000 +Anderson Varejao,282,140,276,1984005 +Andre Drummond,1198,245,1314,22116750 +Andre Iguodala,263,102,457,11131368 +Andre Roberson,251,133,337,2183072 +Andrea Bargnani,97,61,304,323599 +Andrew Bogut,492,221,375,11027027 +Andrew Nicholson,201,69,384,6088993 +Andrew Wiggins,292,165,1675,6006600 +Anthony Bennett,23,8,28,1015696 +Anthony Brown,70,34,115,874636 +Anthony Davis,627,148,1481,22116750 +Anthony Morrow,62,64,380,3488000 +Anthony Tolliver,229,86,383,8000000 +Archie Goodwin,142,84,505,2094089 +Aron Baynes,384,151,514,6500000 +Arron Afflalo,266,142,909,12500000 +Austin Rivers,130,137,593,11000000 +Avery Bradley,220,164,1155,8269663 +Ben McLemore,148,140,531,4008882 +Beno Udrih,144,102,410,1551659 +Bismack Biyombo,655,225,454,17000000 +Blake Griffin,294,95,749,20140838 +Boban Marjanovic,194,54,297,7000000 +Bobby Portis,337,100,436,1453680 +Bojan Bogdanovic,255,119,887,3573020 +Boris Diaw,233,102,485,7000000 +Bradley Beal,188,117,958,22116750 +Brandan Wright,43,21,83,5700000 +Brandon Bass,285,125,473,1551659 +Brandon Jennings,192,114,664,5000000 +Brandon Knight,202,124,1018,12606250 +Brandon Rush,181,57,305,3500000 +Brian Roberts,82,70,410,1050961 +Briante Weber,50,24,62,328000 +Brook Lopez,573,215,1501,21165675 +Bruno Caboclo,2,2,3,1589640 +C.J. McCollum,259,187,1666,3219579 +C.J. Miles,175,142,753,4583450 +C.J. Watson,66,49,142,5000000 +C.J. Wilcox,12,15,70,1209680 +Cameron Payne,85,80,283,2112480 +Carl Landry,146,69,351,6500000 +Carmelo Anthony,556,180,1573,24559380 +Caron Butler,22,19,63,517220 +Chandler Parsons,285,114,837,22116750 +Channing Frye,468,254,850,7806971 +Chris Andersen,196,58,210,1551659 +Chris Bosh,390,101,1010,23741060 +Chris McCullough,68,39,112,1191480 +Chris Paul,310,185,1446,22868828 +Christian Wood,38,11,61,874636 +Clint Capela,494,190,542,1296240 +Cody Zeller,455,204,638,5318313 +Cole Aldrich,288,139,328,7643979 +Corey Brewer,199,168,590,7600000 +Cory Joseph,210,131,677,7330000 +Courtney Lee,410,268,1522,11242000 +Cristiano Felicio,102,31,105,874636 +D.J. Augustin,190,170,934,7250000 +D'Angelo Russell,274,142,1054,5332800 +Damian Lillard,302,165,1879,24328425 +Damjan Rudez,20,33,75,980431 +Danilo Gallinari,281,87,1036,15050000 +Danny Green,303,141,572,10000000 +Dante Cunningham,236,173,484,2978250 +Darrell Arthur,293,185,527,8070175 +Darren Collison,169,142,1035,5229454 +Darrun Hilliard,45,28,152,874060 +David Lee,610,228,854,1551659 +David West,309,142,554,1551659 +DeAndre Jordan,1059,207,980,21165675 +Delon Wright,37,7,103,1577280 +DeMar DeRozan,349,167,1830,26540100 +DeMarcus Cousins,747,236,1748,16957900 +DeMarre Carroll,122,63,286,14200000 +Dennis Schroder,204,138,879,2708582 +Deron Williams,190,155,918,9000000 +Derrick Favors,503,150,1016,11050000 +Derrick Rose,225,84,1080,21323250 +Derrick Williams,296,69,746,4598000 +Devin Booker,187,225,1048,2223600 +Devin Harris,138,124,487,4228000 +Dewayne Dedmon,228,109,255,2898000 +Dion Waiters,202,143,762,2898000 +Dirk Nowitzki,489,161,1372,25000000 +Doug McDermott,195,127,764,2483040 +Draymond Green,769,240,1131,15330435 +Dwight Howard,835,219,976,23180275 +Dwight Powell,273,118,399,8375000 +Dwyane Wade,302,116,1409,23200000 +E'Twaun Moore,133,103,443,8081363 +Ed Davis,599,202,526,6666667 +Elfrid Payton,261,159,778,2613600 +Elliot Williams,4,2,8,250000 +Elton Brand,63,32,70,1000000 +Emmanuel Mudiay,232,141,872,3241800 +Enes Kanter,664,166,1038,17145838 +Eric Bledsoe,125,75,632,14000000 +Eric Gordon,99,98,686,12385364 +Ersan Ilyasova,806,342,1534,8400000 +Evan Fournier,225,214,1213,17000000 +Evan Turner,397,139,854,16393443 +Festus Ezeli,257,94,320,7400000 +Frank Kaminsky,335,126,606,2730000 +Garrett Temple,212,195,582,8000000 +Gary Harris,220,143,932,1655880 +George Hill,298,149,894,8000000 +Gerald Green,163,123,616,1410598 +Gerald Henderson,211,87,624,9000000 +Giannis Antetokounmpo,612,258,1350,2995421 +Glenn Robinson,68,21,171,1050500 +Goran Dragic,277,188,1018,15890000 +Gordon Hayward,397,183,1578,16073140 +Gorgui Dieng,585,219,827,2348783 +Greg Monroe,696,163,1210,17100000 +Greivis Vasquez,47,42,130,4347826 +Harrison Barnes,325,136,774,22116750 +Hassan Whiteside,865,201,1040,22116750 +Henry Sims,72,37,91,75000 +Hollis Thompson,272,187,758,1015696 +Ian Clark,68,58,236,1015696 +Ian Mahinmi,507,220,660,15944154 +Iman Shumpert,203,119,311,9700000 +Isaiah Canaan,174,133,846,1015696 +Isaiah Thomas,243,167,1823,6587132 +Ish Smith,610,266,1944,6000000 +J.J. Barea,152,110,808,4096950 +J.J. Redick,139,135,1226,7377500 +J.R. Smith,217,204,955,12800000 +Jabari Parker,395,154,1070,5374320 +Jae Crowder,373,198,1038,6286408 +Jahlil Okafor,373,140,928,4788840 +Jamal Crawford,145,122,1120,13253012 +Jameer Nelson,115,100,299,4540525 +James Ennis,84,56,318,2898000 +James Harden,501,229,2376,26540100 +James Johnson,126,84,287,4000000 +James Jones,50,50,178,1551659 +James Michael McAdoo,58,39,117,980431 +James Young,26,17,29,1825200 +JaMychal Green,373,189,578,980431 +Jared Dudley,286,190,638,10470000 +Jared Sullinger,673,209,834,5628000 +Jarell Eddie,23,5,63,175000 +Jarell Martin,86,58,153,1286160 +Jarnell Stokes,8,2,14,980431 +Jarrett Jack,136,77,411,1551659 +Jason Smith,219,163,546,5000000 +Jason Terry,79,91,422,1551659 +Jason Thompson,268,160,294,945126 +JaVale McGee,133,49,172,1403611 +Jeff Green,664,328,1878,15000000 +Jeff Teague,214,167,1239,8800000 +Jeff Withey,173,75,217,1015696 +Jerami Grant,361,215,749,980431 +Jeremy Evans,54,19,71,1227286 +Jeremy Lamb,252,105,580,6511628 +Jeremy Lin,253,165,914,11483254 +Jerian Grant,143,97,426,1643040 +Jerryd Bayless,141,111,541,9424084 +Jimmy Butler,358,124,1399,17552209 +Joakim Noah,255,77,124,17000000 +Jodie Meeks,5,2,22,6540000 +Joe Harris,3,1,3,980431 +Joe Ingles,151,98,342,2250000 +Joe Johnson,584,292,1984,11000000 +Joffrey Lauvergne,290,115,465,1709720 +John Henson,221,131,397,12517606 +John Jenkins,114,44,356,1050961 +John Wall,379,159,1531,16957900 +Jon Leuer,373,125,567,10991957 +Jonas Jerebko,288,137,340,5000000 +Jonas Valanciunas,547,158,768,14382022 +Jonathon Simmons,96,103,331,874636 +Jordan Adams,2,2,7,1465080 +Jordan Clarkson,313,168,1225,12500000 +Jordan Farmar,25,22,110,510921 +Jordan Hill,451,155,645,3911380 +Jordan McRae,40,34,198,874636 +Jordan Mickey,13,5,21,1223653 +Jose Calderon,229,135,544,7708427 +Joseph Young,50,30,154,1052342 +Josh Huestis,10,3,14,1191480 +Josh McRoberts,105,68,150,5782450 +Josh Richardson,107,107,341,874636 +Josh Smith,384,236,664,5400000 +Jrue Holiday,195,148,1089,11286518 +Julius Randle,829,242,919,3267120 +Justin Anderson,132,45,208,1514160 +Justin Holiday,180,122,478,1015696 +Justise Winslow,403,184,502,2593440 +Jusuf Nurkic,175,91,263,1921320 +K.J. McDaniels,42,30,89,3333333 +Karl-Anthony Towns,857,245,1503,5960160 +Kawhi Leonard,493,133,1523,17638063 +Kelly Olynyk,281,163,687,3094014 +Kelly Oubre,133,100,236,2006640 +Kemba Walker,357,111,1689,12000000 +Kenneth Faried,581,169,835,12078652 +Kent Bazemore,379,176,872,15730338 +Kentavious Caldwell-Pope,282,167,1105,3678319 +Kevin Durant,589,137,2029,26540100 +Kevin Garnett,150,70,122,8000000 +Kevin Love,762,159,1234,21165675 +Kevin Martin,220,166,1028,1360305 +Kevin Seraphin,126,67,187,1800000 +Kevon Looney,10,2,9,1182840 +Khris Middleton,302,204,1434,15200000 +Klay Thompson,306,152,1771,16663575 +Kosta Koufos,423,185,529,8046500 +Kris Humphries,436,188,686,4000000 +Kristaps Porzingis,526,205,1028,4317720 +Kyle Anderson,244,97,350,1192080 +Kyle Korver,260,161,739,5239437 +Kyle Lowry,365,211,1634,12000000 +Kyle O'Quinn,250,107,309,3900000 +Kyle Singler,141,120,231,4837500 +Kyrie Irving,157,107,1041,17638063 +Lamar Patterson,50,45,84,100000 +LaMarcus Aldridge,632,151,1331,20575005 +Lance Stephenson,446,270,1146,1227286 +Lance Thomas,131,109,484,6191000 +Langston Galloway,288,177,625,5200000 +Larry Nance Jr.,312,124,349,1207680 +Lavoy Allen,424,147,428,4000000 +Leandro Barbosa,117,107,433,4000000 +LeBron James,565,143,1920,30963450 +Lou Amundson,49,44,52,1551659 +Lou Williams,169,107,1028,7000000 +Luc Mbah a Moute,171,100,231,2203000 +Lucas Nogueira,46,29,65,1921320 +Luis Scola,360,178,664,5000000 +Luke Babbitt,148,90,327,1227000 +Luol Deng,443,122,911,18000000 +Manu Ginobili,146,99,555,14000000 +Marc Gasol,362,141,861,21165675 +Marcelo Huertas,90,76,237,1500000 +Marcin Gortat,741,193,1012,12000000 +Marco Belinelli,117,91,696,6333333 +Marcus Morris,404,170,1131,4625000 +Marcus Smart,255,183,558,3578880 +Marcus Thornton,300,180,1180,1315448 +Mario Hezonja,176,124,478,3909840 +Markieff Morris,704,368,1530,7400000 +Marreese Speights,239,117,512,1403611 +Marvin Williams,521,133,948,12250000 +Mason Plumlee,628,253,749,2328530 +Matt Barnes,420,234,758,6125000 +Matthew Dellavedova,162,178,569,9607500 +Maurice Harkless,280,124,498,8988764 +Metta World Peace,89,67,176,1551659 +Meyers Leonard,314,183,511,9213484 +Michael Beasley,98,39,255,1403611 +Michael Carter-Williams,276,162,623,3183526 +Michael Kidd-Gilchrist,45,9,89,13000000 +Mike Conley,164,100,857,26540100 +Mike Dunleavy,83,66,222,4837500 +Mike Miller,53,53,63,3500000 +Mike Muscala,117,72,195,1015696 +Mike Scott,206,102,464,3333334 +Miles Plumlee,232,73,314,12500000 +Mirza Teletovic,302,159,965,10500000 +Mitch McGary,18,10,26,1526040 +Mo Williams,72,60,338,2200000 +Monta Ellis,271,174,1121,10770000 +Montrezl Harrell,65,48,140,1000000 +Myles Turner,332,158,619,2463840 +Nemanja Bjelica,210,154,308,3800000 +Nene Hilario,259,156,525,2898000 +Nerlens Noel,542,192,747,4384490 +Nick Collison,171,107,123,3750000 +Nick Young,97,50,392,5443918 +Nicolas Batum,428,114,1046,20869566 +Nik Stauskas,183,115,617,2993040 +Nikola Jokic,560,208,796,1358500 +Nikola Mirotic,362,151,777,5782450 +Nikola Pekovic,21,22,54,12100000 +Nikola Vucevic,576,173,1181,11750000 +Noah Vonleh,305,152,283,2751360 +Norman Powell,111,59,273,874636 +Omer Asik,414,124,269,9904494 +Omri Casspi,410,154,813,3000000 +Otto Porter,391,163,871,5893981 +P.J. Tucker,512,202,652,5300000 +Pablo Prigioni,114,82,145,1050961 +Pat Connaughton,32,6,36,874636 +Patrick Beverley,250,234,700,6000000 +Patrick Mills,158,102,690,3578948 +Patrick Patterson,342,128,543,6050000 +Pau Gasol,793,148,1187,15500000 +Paul George,563,230,1874,18314532 +Paul Millsap,732,237,1385,20072033 +Paul Pierce,186,129,417,3500000 +Phil Pressey,62,56,154,35000 +Quincy Acy,188,103,307,1050961 +R.J. Hunter,37,29,97,1200240 +Rajon Rondo,435,175,859,14000000 +Rakeem Christmas,1,1,4,1052342 +Ramon Sessions,203,102,810,6000000 +Randy Foye,312,262,948,2500000 +Rashad Vaughn,88,73,217,1811040 +Raul Neto,120,122,476,937800 +Raymond Felton,258,141,763,1551659 +Reggie Bullock,68,29,122,2255644 +Reggie Jackson,253,187,1489,14956522 +Richard Jefferson,128,129,410,2500000 +Richaun Holmes,135,92,287,1025831 +Ricky Rubio,326,201,766,13550000 +Robert Covington,419,232,858,1015696 +Robin Lopez,602,182,842,13219250 +Rodney Hood,267,202,1149,1406520 +Rodney Stuckey,156,77,517,7000000 +Rondae Hollis-Jefferson,154,61,167,1395600 +Ronnie Price,99,161,331,2557545 +Roy Hibbert,398,252,481,5000000 +Rudy Gay,456,184,1204,13333333 +Rudy Gobert,668,163,557,2121288 +Russell Westbrook,626,200,1878,26540100 +Ryan Anderson,396,109,1124,18735364 +Ryan Kelly,121,39,152,0 +Salah Mejri,123,54,125,874636 +Sam Dekker,1,0,0,1720560 +Sasha Kaun,26,11,23,1333420 +Sasha Vujacic,149,83,296,1410598 +Sean Kilpatrick,112,74,690,980431 +Serge Ibaka,533,211,983,12250000 +Seth Curry,60,41,299,2898000 +Shabazz Muhammad,267,94,863,3046299 +Shabazz Napier,55,38,203,1350120 +Shaun Livingston,169,140,488,5782450 +Shelvin Mack,256,144,896,2433334 +Solomon Hill,166,68,246,11241218 +Spencer Hawes,244,111,340,6348759 +Stanley Johnson,306,178,593,2969880 +Stephen Curry,430,161,2375,12112359 +Steve Novak,10,6,48,1551659 +Steven Adams,533,223,636,3140517 +T.J. McConnell,250,114,493,874636 +T.J. Warren,147,94,515,2128920 +Taj Gibson,504,180,627,8950000 +Tarik Black,157,78,133,6191000 +Terrence Jones,211,86,434,1050961 +Terrence Ross,185,120,720,10000000 +Terry Rozier,63,23,72,1906440 +Thabo Sefolosha,334,109,480,3850000 +Thaddeus Young,660,182,1102,14153652 +Thomas Robinson,359,111,307,1050961 +Tiago Splitter,120,71,201,8550000 +Tibor Pleiss,15,19,24,3000000 +Tim Duncan,447,125,522,1881250 +Tim Frazier,220,146,522,2090000 +Tim Hardaway,86,48,326,2281605 +Timofey Mozgov,337,159,475,16000000 +Tobias Harris,1020,306,2232,17200000 +Tony Allen,296,175,535,5505618 +Tony Parker,176,114,857,14445313 +Tony Snell,199,100,338,2368327 +Tony Wroten,21,11,67,25000 +Trevor Ariza,366,177,1025,7806971 +Trevor Booker,452,169,463,9250000 +Trey Burke,112,86,679,3386598 +Trey Lyles,299,114,488,2340600 +Tristan Thompson,738,202,643,15330435 +Troy Daniels,57,40,242,3332940 +Ty Lawson,240,174,746,1315448 +Tyler Ennis,75,64,206,1733880 +Tyler Johnson,109,66,314,5628000 +Tyler Zeller,178,97,364,8000000 +Tyreke Evans,129,66,379,10203755 +Tyson Chandler,576,145,472,12415000 +Tyus Jones,47,33,156,1339680 +Udonis Haslem,75,25,59,4000000 +Victor Oladipo,345,174,1153,6552960 +Vince Carter,146,101,395,4264057 +Walter Tavares,21,8,25,1000000 +Wayne Ellington,174,82,586,6000000 +Wesley Johnson,248,173,549,5628000 +Wesley Matthews,238,179,977,17100000 +Will Barton,476,147,1178,3533333 +Willie Cauley-Stein,352,146,463,3551160 +Willie Reed,122,47,182,1015696 +Zach LaVine,228,193,1150,2240880 +Zach Randolph,529,146,1040,10361445 diff --git a/10-04/GMQ.ipynb b/10-04/GMQ.ipynb new file mode 100644 index 0000000..306cacd --- /dev/null +++ b/10-04/GMQ.ipynb @@ -0,0 +1,1224 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5ac73f99-5376-4acb-a734-f2f286db9081", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "gmq (generic function with 1 method)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "include(\"GMQ.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5ea19dd1-068f-46c4-b41e-21b05bc925ef", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient method for quadratic functions (optimal stepsize)\n", + "iter\tf(x)\t\t\t||g||\t\talpha\n", + "\n", + " 1\t0.00000000e+00\t\t1.1180e+01\t2.27e-01\n", + " 2\t-1.42045455e+01\t\t3.0492e+00\t1.32e-01\n", + " 3\t-1.48162244e+01\t\t4.8145e-01\t2.27e-01\n", + " 4\t-1.48425647e+01\t\t1.3130e-01\t1.32e-01\n", + " 5\t-1.48436990e+01\t\t2.0732e-02\t2.27e-01\n", + " 6\t-1.48437478e+01\t\t5.6543e-03\t1.32e-01\n", + " 7\t-1.48437499e+01\t\t8.9278e-04\t2.27e-01\n", + " 8\t-1.48437500e+01\t\t2.4349e-04\t1.32e-01\n", + " 9\t-1.48437500e+01\t\t3.8445e-05\t2.27e-01\n", + " 10\t-1.48437500e+01\t\t1.0485e-05\t1.32e-01\n", + " 11\t-1.48437500e+01\t\t1.6555e-06\t2.27e-01\n", + " 12\t-1.48437500e+01\t\t4.5151e-07\n" + ] + }, + { + "data": { + 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+ { + "data": { + "text/plain": [ + "([-2.1874999648171993, -1.5624999537068414], \"optimal\")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Q = [6 -2; -2 6];\n", + "q = [10; 5];\n", + "range = ((-3, -3), (-1, 0));\n", + "plt = plotQ(Q, q, range);\n", + "plt |> display\n", + "gmq(Q, q, x=[-1;0]; plt=plt, Plotf=2, printing=false)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2d5bef71-41c8-407b-a282-793cee40b68a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "genQf (generic function with 1 method)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "include(\"genQf.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bf4039cc-6753-4e73-a758-6ecb31c1399d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2×2 Matrix{Float64}:\n", + " 0.000872202 0.000493966\n", + " 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"output_type": "execute_result" + } + ], + "source": [ + "Q, q, v = genQf(2, v=0, ecc=0.5)\n", + "map(display, [Q, q, v])\n", + "plt = plotQ(Q, q, ((-10, -10), (10, 10)))\n", + "plt |> display\n", + "gmq(Q, q; plt=plt, Plotf=2, printing=false)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4af2f35f-3c04-4860-89e6-9d23fc2fb34f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-04/GMQ.jl b/10-04/GMQ.jl new file mode 100644 index 0000000..e6ddc7e --- /dev/null +++ b/10-04/GMQ.jl @@ -0,0 +1,247 @@ +using LinearAlgebra +using Printf +using Plots + +function gmq(Q::Matrix, q::Vector; x::Union{Vector, Nothing}=nothing, fStar::Real=-Inf, alpha::Real=0 , MaxIter::Int=1000 , eps::Real=1e-6, plt::Union{Plots.Plot, Nothing}=nothing, Plotf::Int=2, printing::Bool=true)::Tuple{Vector, String} + # Plotf + # 0 = nothing is plotted + # 1 = the function value / gap are plotted + # 2 = the level sets of f and the trajectory are plotted (when n = 2) + + Interactive = true # if we pause at every iteration + + Streamlined = true # if the streamlined version of the algorithm, with + # only one O( n^2 ) operation per iteration, is used + + # reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if !isreal(Q) + throw(ArgumentError(Q, "Q not a real matrix")) + end + + n = size(Q, 1) + + if n <= 1 + throw(ArgumentError(Q, "Q is too small")) + end + + if n != size(Q, 2) + throw(ArgumentError(Q, "Q is not square")) + end + + if !isreal(q) + throw(ArgumentError(q, "q not a real vector")) + end + + if size(q, 1) != n + throw(ArgumentError(q, "q size does not match with Q")) + end + + if x == nothing + x = zeros(n, 1) + end + + if !isreal(x) + throw(ArgumentError(x, "x not a real vector")) + end + + if size(x, 1) != n + throw(ArgumentError(x, "x size does not match with Q")) + end + + if MaxIter < 1 + throw(ArgumentError(MaxIter, "MaxIter too small")) + end + + if eps < 0 + throw(ArgumentError(eps, "eps can not be negative")) + end + + # initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if printing + print("Gradient method for quadratic functions ") + if alpha == 0 + print("(optimal stepsize)\n") + else + print("(fixed stepsize)\n") + end + + print("iter\tf(x)\t\t\t||g||") + end + + if fStar > - Inf + if printing + print("\t\tgap\t\trate") + end + prevf = Inf + end + if printing + if alpha == 0 + print("\t\talpha") + end + + print("\n\n") + end + + i = 0; + if Plotf == 1 + gap = [] + end + + if Streamlined + g = Q * x + q + end + + if Plotf == 1 && plt == nothing + plt = plot(yscale = :log, + xlims=(0, MaxIter), + ylims=(1e-15, Inf), + guidefontsize=16) + elseif Plotf == 2 && plt == nothing + plt = plot() + end + + status = "" + + # main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + while true + if !Streamlined + g = Q * x + q + end + + ng = norm(g) + + f = dot((g + q)', x) / 2 # 1/2 x^T Q x + q x + # = 1/2 ( x^T Q x + 2 q x ) + # = 1/2 x^T ( Q x + q + q ) + # = 1/2 ( q + g ) x + i += 1 + + if printing + @printf("%4d\t%1.8e\t\t%1.4e", i, f, ng) + end + if fStar > -Inf + gapk = (f - fStar)/maximum([abs(fStar), 1]) + if printing + @printf("\t%1.4e", gapk) + + if prevf < Inf + @printf("\t%1.4e", (f - fStar)/(prevf - fStar)) + else + @printf("\t\t") + end + end + + prevf = f + + if Plotf == 1 + push!(gap, gapk) + end + end + + # stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + if ng <= eps + status = "optimal" + if alpha == 0 && printing + print("\n") + end + break + end + + if i > MaxIter + status = "stopped" + if alpha == 0 && printing + print("\n") + end + break + end + + # compute step size - - - - - - - - - - - - - - - - - - - - - - - - - - + # meanwhile, check if f is unbounded below + # note that if alpha > 0 this is only used for the unboundedness check + # which is a bit of a waste, but there you go; anyway, in the + # streamlined version this only costs O( n ) + + if Streamlined + v = Q * g; + den = dot(g', v) + else + den = dot(g', Q * g) + end + + if den <= 1e-14 + # this is actually two different cases: + # - g' * Q * g = 0, i.e., f is linear along g, and since the + # gradient is not zero, it is unbounded below + # + # - g' * Q * g < 0, i.e., g is a direction of negative curvature for + # f, which is then necessarily unbounded below + if printing + if alpha == 0 + print("\n") + end + @printf("g' * Q * g = %1.4e ==> unbounded\n", den) + end + status = "unbounded" + break + end + + if alpha > 0 + t = alpha + else + t = ng^2 / den # stepsize + if printing + @printf("\t%1.2e", t) + end + end + + if printing + print("\n") + end + + # compute new point - - - - - - - - - - - - - - - - - - - - - - - - - - + + # possibly plot the trajectory + if n == 2 && Plotf == 2 + PXY = hcat(vec(x), vec(x - t * g)) + plot!(PXY[1,:], + PXY[2,:], + linestyle=:solid, + linewidth=2, + markershape=:circle, + seriescolor=colorant"black", + label="") + end + + x = x - t * g + + if Streamlined + g = g - t * v + end + + if Interactive + #readline() + end + + if Plotf != 0 + #IJulia.clear_output(true) + #display(plt) + end + end + + if Plotf == 1 + plot!(plt, + gap, + linewidth=2, + seriescolor=colorant"black") + display(plt) + elseif Plotf == 2 + display(plt) + end + (vec(x), status) +end \ No newline at end of file diff --git a/10-04/GMQ.m b/10-04/GMQ.m new file mode 100644 index 0000000..b12fabb --- /dev/null +++ b/10-04/GMQ.m @@ -0,0 +1,319 @@ +function [ x , status ] = GMQ( Q , q , varargin ) + +%function [ x , status ] = GMQ( Q , q , x , fStar , alpha , MaxIter , eps ) +% +% Apply the Gradient Method (a.k.a., Steepest Descent algorithm) to the +% minimization of the quadratic function +% +% f( x ) = 1/2 x^T Q x + q x +% +% Input: +% +% - Q ([ n x n ] real symmetric matrix, not necessarily positive +% semidefinite): the quadratic part of f +% +% - q ([ n x 1 ] real column vector): the linear part of f +% +% - x ([ n x 1 ] real column vector or empty, optional): the point where to +% start the algorithm from; if not provided or empty, the all-0 n-vector +% is used +% +% - fStar (real scalar, optional, default value Inf): optimal value of f. +% if a non-Inf value is provided it is used to print out stasistics about +% the convergence speed of the algorithm +% +% - alpha (real scalar, optional, default value 0): if alpha > 0, then the +% fixed-stepsize version of the algorithm is run with alpha as the fixed +% stepsize, otherwise the standard exact line search is used +% +% - MaxIter (integer scalar, optional, default value 1000): the maximum +% number of iterations +% +% - eps (real scalar, optional, default value 1e-6): the accuracy in the +% stopping criterion: the algorithm is stopped when the norm of the +% gradient is less than or equal to eps +% +% Output: +% +% - x ([ n x 1 ] real column vector): either the best solution found so far +% (possibly the optimal one) or a direction proving the problem is +% unbounded below, depending on case +% +% - status (string): a string describing the status of the algorithm at +% termination +% +% = 'optimal': the algorithm terminated having proven that x is a(n +% approximately) optimal solution, i.e., the norm of the gradient at x +% is less than the required threshold +% +% = 'unbounded': the algorithm terminated having proven that the problem +% is unbounded below: x contains a direction along which f is +% decreasing to - Inf, either because f is linear along x and the +% directional derivative is not zero, or because x is a direction with +% negative curvature +% +% = 'stopped': the algorithm terminated having exhausted the maximum +% number of iterations: x is the best solution found so far, but not +% necessarily the optimal one +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 26-12-22 + Version 0.2 + Copyright Antonio Frangioni + ======================================= +%} + +Plotf = 2; +% 0 = nothing is plotted +% 1 = the function value / gap are plotted +% 2 = the level sets of f and the trajectory are plotted (when n = 2) + +Interactive = true; % if we pause at every iteration + +Streamlined = true; % if the streamlined version of the algorithm, with + % only one O( n^2 ) operation per iteration, is used + +% reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +if ~ isreal( Q ) + error( 'Q not a real matrix' ); +end + +n = size( Q , 1 ); + +if n <= 1 + error( 'Q is too small' ); +end + +if n ~= size( Q , 2 ) + error( 'Q is not square' ); +end + +if ~ isreal( q ) + error( 'q not a real vector' ); +end + +if size( q , 2 ) ~= 1 + error( 'q is not a (column) vector' ); +end + +if size( q , 1 ) ~= n + error( 'q size does not match with Q' ); +end + +if isempty( varargin ) || isempty( varargin{ 1 } ) + x = zeros( n , 1 ); +else + x = varargin{ 1 }; + + if ~ isreal( x ) + error( 'x not a real vector' ); + end + + if size( x , 2 ) ~= 1 + error( 'x is not a (column) vector' ); + end + + if size( x , 1 ) ~= n + error( 'x size does not match with Q' ); + end +end + +if length( varargin ) > 1 + fStar = varargin{ 2 }; + if ~ isreal( fStar ) || ~ isscalar( fStar ) + error( 'fStar is not a real scalar' ); + end +else + fStar = - Inf; +end + +if length( varargin ) > 2 + alpha = varargin{ 3 }; + if ~ isreal( alpha ) || ~ isscalar( alpha ) + error( 'alpha is not a real scalar' ); + end +else + alpha = 0; +end + +if length( varargin ) > 3 + MaxIter = round( varargin{ 4 } ); + if ~ isscalar( MaxIter ) + error( 'MaxIter is not an integer scalar' ); + end + if MaxIter < 1 + error( 'MaxIter too small' ); + end +else + MaxIter = 1000; +end + +if length( varargin ) > 4 + eps = varargin{ 5 }; + if ~ isreal( eps ) || ~ isscalar( eps ) + error( 'eps is not a real scalar' ); + end + if eps < 0 + error( 'eps can not be negative' ); + end +else + eps = 1e-6; +end + +% initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +fprintf( 'Gradient method for quadratic functions ' ); +if alpha == 0 + fprintf( '(optimal stepsize)\n' ); +else + fprintf( '(fixed stepsize)\n' ); +end +fprintf( 'iter\tf(x)\t\t\t||g||'); +if fStar > - Inf + fprintf( '\t\tgap\t\trate'); + prevf = Inf; +end +if alpha == 0 + fprintf( '\t\talpha' ); +end +fprintf( '\n\n' ); + +i = 0; + +if Plotf == 1 + gap = []; +end + +if Streamlined + g = Q * x + q; +end + +% main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +while true + % compute function value and gradient - - - - - - - - - - - - - - - - - + + if ~ Streamlined + g = Q * x + q; + end + + ng = norm( g ); + f = ( g + q )' * x / 2; % 1/2 x^T Q x + q x = 1/2 ( x^T Q x + 2 q x ) + % = 1/2 x^T ( Q x + q + q ) = 1/2 ( q + g ) x + i = i + 1; + + % output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - - + + fprintf( '%4d\t%1.8e\t\t%1.4e' , i , f , ng ); + if fStar > - Inf + gapk = ( f - fStar ) / max( [ abs( fStar ) 1 ] ); + + fprintf( '\t%1.4e' , gapk ); + if prevf < Inf + fprintf( '\t%1.4e' , ( f - fStar ) / ( prevf - fStar ) ); + else + fprintf( '\t\t' ); + end + prevf = f; + + if Plotf == 1 + gap( end + 1 ) = gapk; + semilogy( gap , 'Color' , 'k' , 'LineWidth' , 2 ); + xlim( [ 0 MaxIter ] ); + ylim( [ 1e-15 inf ] ); + ax = gca; + ax.FontSize = 16; + ax.Position = [ 0.03 0.07 0.95 0.92 ]; + ax.Toolbar.Visible = 'off'; + end + end + + % stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + if ng <= eps + status = 'optimal'; + if alpha == 0 + fprintf( '\n' ); + end + break; + end + + if i > MaxIter + status = 'stopped'; + if alpha == 0 + fprintf( '\n' ); + end + break; + end + + % compute step size - - - - - - - - - - - - - - - - - - - - - - - - - - + % meanwhile, check if f is unbounded below + % note that if alpha > 0 this is only used for the unboundedness check + % which is a bit of a waste, but there you go; anyway, in the + % streamlined version this only costs O( n ) + + if Streamlined + v = Q * g; + den = g' * v; + else + den = g' * Q * g; + end + + if den <= 1e-14 + % this is actually two different cases: + % + % - g' * Q * g = 0, i.e., f is linear along g, and since the + % gradient is not zero, it is unbounded below + % + % - g' * Q * g < 0, i.e., g is a direction of negative curvature for + % f, which is then necessarily unbounded below + % + if alpha == 0 + fprintf( '\n' ); + end + fprintf( 'g'' * Q * g = %1.4e ==> unbounded\n' , den ); + status = 'unbounded'; + break; + end + + if alpha > 0 + t = alpha; + else + t = ng^2 / den; % stepsize + fprintf( '\t%1.2e' , t ); + end + + fprintf( '\n' ); + + % compute new point - - - - - - - - - - - - - - - - - - - - - - - - - - + + % possibly plot the trajectory + if n == 2 && Plotf == 2 + PXY = [ x , x - t * g ]; + line( 'XData' , PXY( 1 , : ) , 'YData' , PXY( 2 , : ) , ... + 'LineStyle' , '-' , 'LineWidth' , 2 , 'Marker' , 'o' , ... + 'Color' , [ 0 0 0 ] ); + end + + x = x - t * g; + + if Streamlined + g = g - t * v; + end + + % iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if Interactive + pause; + end +end + +% end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end % the end- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/10-04/genQf.jl b/10-04/genQf.jl new file mode 100644 index 0000000..49795ea --- /dev/null +++ b/10-04/genQf.jl @@ -0,0 +1,111 @@ +using Random +using LinearAlgebra + +function genQf(n::Integer; fseed::Integer=0, rank::Real=1.1, conv::Real=1, ecc::Real=0.99, dom::Real=1, box::Real=1, q::Union{Vector, Nothing}=nothing, v::Union{Real, Nothing}=nothing)::Tuple{Matrix, Union{Vector, Nothing}, Union{Real, Nothing}} + if n <= 0 + throw(ArgumentError(n, "n must be > 0")) + end + if rank <= 0 + throw(ArgumentError(rank, "rank must be > 0")) + end + if !(0 <= conv <= 1) + throw(ArgumentError(conv, "conv must be in [0, 1]")) + end + if !(0 <= ecc < 1) + throw(ArgumentError(ecc, "ecc must be in [0, 1)")) + end + if dom < 0 + throw(ArgumentError(dom, "dom must be >= 0")) + end + if box == 0 + throw(ArgumentError(box, "box must not be 0")) + end + + Random.seed!(fseed) + + # generate Q- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # first step: generate the appropriate rank and positive / negative + # definiteness + + r = round(Int, rank * n) + p = round(Int, r * conv) + + if p > 0 + G = rand(p, n) + Q = G' * G + else + Q = zeros(n, n) + end + + if r > p + G = rand(r - p, n) + Q = Q - (G' * G) + end + + # second step: if dom ~= 1, modify the diagonal + # increase or decrease randomly each element by [ - 1/3 , 1/3 ] of its + # initial value, then multiply it by dom + + if dom != 1 + D = diag(Q) + D = D .* (1 .+ (2 .* rand(n, 1) .- 1) ./ 3) + D = dom * D + @view(Q[diagind(Q)]) .= D + end + + # compute eigenvalue decomposition + F = eigen(Q); # V * D * V' = Q , D( 1 , 1 ) = \lambda_n + V = F.vectors + D = F.values + + if D[1] > 1e-14 + # modify eccentricity only if \lambda_n > 0, for when \lambda_n = 0 the + # eccentricity is 1 by default + # + # the formula is: + # + # \lambda_i - \lambda_n 2 * ecc + # \lambda_i = \lambda_n + --------------------- * \lambda_n ------- + # \lambda_1 - \lambda_n 1 - ecc + # + # This leaves \lambda_n unchanged, and modifies all the other ones + # proportionally so that + # + # \lambda_1 - \lambda_n + # --------------------- = ecc (exercise: check) + # \lambda_1 - \lambda_n + l = D[1] .* ones(n) + ((D[1] / (D[n] - D[1])) * (2 * ecc / (1 - ecc))) .* (D - D[1] .* ones(n)) + Q = V * diagm(l) * V'; + end + + if q != nothing || v != nothing + # if so required generate q- - - - - - - - - - - - - - - - - - - - - - - + # + # we first generate the unconstrained minimum x_* of the problem in the + # box [ - abs( box ) , abs( box ) ] and then we set q = - Q * x_* + + x = 2 * abs(box) .* rand(n) .- abs(box) + q = -Q * x + + # if so required, we now randomly destroy the alignment between q and + # the image of Q so as to make it hard to solve Q x = q + + if ( box < 0 ) && (D[1] <= 1e-14 ) + q = q .* ((4/3) .* rand(n) .- (2/3)) + end + end + + if v != nothing + # if so required compute v. - - - - - - - - - - - - - - - - - - - - - + # v is finite-valued only if either Q is strictly positive definite + # or it is positive semidefinite but q has been constructed in such + # a was that Q * x + q = 0 has a solution (that is the x we have) + if (D[1] > 1e-14) || ((D[1] > -1e-14) && (box > 0)) + v = dot(q', x) / 2 + else + v = -Inf + end + end + + return (Q, q, v) +end \ No newline at end of file diff --git a/10-04/getQf.m b/10-04/getQf.m new file mode 100644 index 0000000..b17a8d5 --- /dev/null +++ b/10-04/getQf.m @@ -0,0 +1,278 @@ +function [ Q , varargout ] = genQf( n , varargin ) + +%function [ Q , q , v ] = genQf( n , seed , rank , conv , ecc , dom , box ) +% +% Produces the data structure encoding a Quadratic function +% +% f( x ) = (1/2) x^T * Q * x + q * x +% +% Input: +% +% - n (integer, scalar): the size of the problem +% +% - seed (integer, default 0): the seed for the random number generator +% +% - rank (real, scalar, default 1.1): Q will be obtained as Q = G^T G, with +% G a m \times n random matrix with m = rank * n (almost, see "conv" +% below). If rank > 1 then Q can be expected to be full-rank, if +% rank < 1 it will not +% +% - conv (real, scalar, default 1): a parameter controlling whether Q is +% positive (semi)definite, negative (semi)definite, or indefinite. This +% is done by actually computing Q as Q = G_+^T G_+ - G_-^T G_-. The m +% rows of G are partitioned according to conv, i.e., m * conv rows are +% put in G_+ while m * ( 1 - conv ) rows are put in G_-. Hence, conv = 1 +% means that Q is positive (semi)definite, conv = 1 means that Q is +% negative (semi)definite, and any value in the middle means that Q is +% indefinite, with "close to 1" meaning "almost positive (semi)definite", +% and "close to 0" meaning "almost negative (semi)definite" +% +% - ecc (real, scalar, default 0.99): the eccentricity of Q, i.e., the +% ratio ( \lambda_1 - \lambda_n ) / ( \lambda_1 + \lambda_n ), with +% \lambda_1 the largest eigenvalue and \lambda_n the smallest one. Note +% that this makes sense only if \lambda_n > 0, for otherwise the +% eccentricity is always 1; hence, this setting is ignored if +% \lambda_n = 0, i.e., Q is not full-rank (see above). An eccentricity of +% 0 means that all eigenvalues are equal, as eccentricity -> 1 the +% largest eigenvalue gets larger and larger w.r.t. the smallest one +% +% - dom (real, scalar, default 1): possibly modifies the matrix by making +% it more or less diagonally dominant. This is obtained by multiplying +% each diagonal element by a random number uniformly picked in the +% interval [ 2 * diag / 3 , 4 * diag / 3 ] (and therefore with average +% value diag). If diag >> 1 this increases the diagonal dominance, if +% diag << 1 this decreases it. The operation is only performed if +% diag ~= 1. +% +% - box (real, scalar, default 1): this parameter controls the generation +% of q. This is done by first randomly generating one (tentatively) +% optimal solution x_* to the unconstrained minimization of f( x ) in the +% symmetric hypercube of size 2 * abs( box ), i.e., by uniformly drawing +% each of its entries in the interval [ - abs( box ) , abs( box ) ]; +% then, q is generated as - Q * x_*, so that Q * x_* + q = 0. This +% ensures that f( x ) is bounded below if Q is positive semidefinite, and +% bounded above if Q is negative semidefinite. However, we also want to +% be able to create problems that are positive semidefinite but not +% bounded below, which means that the system Q x + q = 0 must not have a +% solution. This first of all requires Q to be low-rank (some of the +% eigenvalues actually = 0), which can be obtained by putting rank < 1. +% Then, if box < 0 the vector q obtained as above is modified by +% multiplying each of its entries by a random number uniformly picked in +% the interval [ 2 / 3 , 4 / 3 ], which makes it unlikely that the system +% Q x = q still has a solution. However, this makes sense only if Q is +% rank-deficient, hence this is done only if Q is not strictly positive +% definite (for otherwise the system always has a solution). +% +% Output: +% +% - Q: n \times n symmetric real matrix, which is either positive +% (semi)definite, negative (semi)definite, or indefinite according +% to the value of the conv parameter +% +% - q: n \times 1 real vector, optional +% +% - v: real, optional: the optimal value of the optimizattion problem +% min{ f( x ) : x \in \R^n }. This is -\infty if Q is not positive +% semidefinite (see conv above). It is -\infty even if Q is positive +% semidefinite but rank deficient (see rank above) and the system +% Q x + q = 0 has no solution. If instead Q is positive semidefinite +% (whatever its rank) and the system Q x + q = 0 has a solution x_*, +% then x_* is an optimal solution to the problem and +% +% v = f( x_* ) = (1/2) x_*^T * Q * x_* + q * x_* +% = (1/2) [ x_*^T * Q * x_* + q * x_* ] + (1/2) q * x_* +% = (1/2) x_*^T ( Q * x_* + q * ) + (1/2) q * x_* +% = (1/2) q * x_* +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 15-08-22 + Version 0.10 + Copyright Antonio Frangioni + ======================================= +%} + +if ~ isscalar( n ) || ~ isreal( n ) + error( 'n not a real scalar' ); +end +n = round( n ); +if n <= 0 + error( 'n must be > 0' ); +end + +if isempty( varargin ) + seed = 0; +else + seed = varargin{ 1 }; + if ~ isscalar( seed ) || ~ isreal( seed ) + error( 'actv not a real scalar' ); + end + + seed = round( seed ); +end + +if length( varargin ) > 1 + rank = varargin{ 2 }; + if ~ isscalar( rank ) || ~ isreal( rank ) + error( 'rank not a real scalar' ); + end + + if rank <= 0 + error( 'rank must be > 0' ); + end +else + rank = 1.1; +end + +if length( varargin ) > 2 + conv = varargin{ 3 }; + if ~ isscalar( conv ) || ~ isreal( conv ) + error( 'conv not a real scalar' ); + end + + if ( conv < 0 ) || ( conv > 1 ) + error( 'conv must be in [0, 1)' ); + end +else + conv = 1; +end + +if length( varargin ) > 3 + ecc = varargin{ 4 }; + if ~ isscalar( ecc ) || ~ isreal( ecc ) + error( 'ecc not a real scalar' ); + end + + if ecc < 0 || ecc >= 1 + error( 'ecc must be in [0, 1)' ); + end +else + ecc = 0.99; +end + +if length( varargin ) > 4 + dom = varargin{ 5 }; + if ~ isscalar( dom ) + error( 'dom not a scalar' ); + end + + if dom < 0 + error( 'dom must be >= 0' ); + end +else + dom = 1; +end + +if length( varargin ) > 5 + box = varargin{ 6 }; + if ~ isscalar( box ) + error( 'box not a scalar' ); + end + + if box == 0 + error( 'box must not be 0' ); + end +else + box = 1; +end + +rng( seed ); + +% generate Q- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% first step: generate the appropriate rank and positive / negative +% definiteness + +r = round( rank * n ); +p = round( r * conv ); + +if p > 0 + G = rand( p , n ); + Q = G' * G; +else + Q = zeros( n , n ); +end + +if r > p + G = rand( r - p , n ); + Q = Q - G' * G; +end + +% second step: if dom ~= 1, modify the diagonal +% increase or decrease randomly each element by [ - 1/3 , 1/3 ] of its +% initial value, then multiply it by dom +if dom ~= 1 + D = diag( Q ); + D = D .* ( 1 + ( 2 * rand( n , 1 ) - 1 ) / 3 ); + D = dom * D; + Q = spdiags( D , 0 , Q ); + Q = full( Q ); +end + +% compute eigenvalue decomposition +[ V , D ] = eig( Q ); % V * D * V' = Q , D( 1 , 1 ) = \lambda_n + +if D( 1 , 1 ) > 1e-14 + % modify eccentricity only if \lambda_n > 0, for when \lambda_n = 0 the + % eccentricity is 1 by default + % + % the formula is: + % + % \lambda_i - \lambda_n 2 * ecc + % \lambda_i = \lambda_n + --------------------- * \lambda_n ------- + % \lambda_1 - \lambda_n 1 - ecc + % + % This leaves \lambda_n unchanged, and modifies all the other ones + % proportionally so that + % + % \lambda_1 - \lambda_n + % --------------------- = ecc (exercise: check) + % \lambda_1 - \lambda_n + + d = diag( D ); + l = d( 1 ) * ones( n , 1 ) + ( d( 1 ) / ( d( n ) - d( 1 ) ) ) * ... + ( 2 * ecc / ( 1 - ecc ) ) * ... + ( d - d( 1 ) * ones( n , 1 ) ); + + Q = V * diag( l ) * V'; +end + +if nargout > 0 + % if so required generate q- - - - - - - - - - - - - - - - - - - - - - - + % + % we first generate the unconstrained minimum x_* of the problem in the + % box [ - abs( box ) , abs( box ) ] and then we set q = - Q * x_* + + x = 2 * abs( box ) * rand( n , 1 ) - abs( box ); + + q = - Q * x; + + % if so required, we now randomly destroy the alignment between q and + % the image of Q so as to make it hard to solve Q x = q + + if ( box < 0 ) && ( D( 1 , 1 ) <= 1e-14 ) + q = q .* ( ( 4 / 3 ) * rand( n , 1 ) - 2 / 3 ); + end + + varargout{ 1 } = q; + + if nargout > 1 + % if so required compute v. - - - - - - - - - - - - - - - - - - - - - + % v is finite-valued only if either Q is strictly positive definite + % or it is positive semidefinite but q has been constructed in such + % a was that Q * x + q = 0 has a solution (that is the x we have) + + if ( D( 1 , 1 ) > 1e-14 ) || ... + ( ( D( 1 , 1 ) > -1e-14 ) && ( box > 0 ) ) + v = q' * x / 2; + else + v = - Inf; + end + + varargout{ 2 } = v; + end +end + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end diff --git a/10-04/lesson.ipynb b/10-04/lesson.ipynb new file mode 100644 index 0000000..bf52cc2 --- /dev/null +++ b/10-04/lesson.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a88398a0-03e1-458f-ba48-518fb151b1b3", + "metadata": {}, + "outputs": [], + "source": [ + "using Plots\n", + "using LinearAlgebra" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "deac13e6-b3dd-4c8d-9c15-504052e60c6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3×3 Matrix{Float64}:\n", + " 0.693988 0.0373642 0.306196\n", + " 0.475249 0.0523086 0.525352\n", + " 0.275428 0.0212671 0.431897" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = rand(3,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a7cfa41c-b406-475b-8a8e-bc56cab68cfa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVD{Float64, Float64, Matrix{Float64}, Vector{Float64}}\n", + "U factor:\n", + "3×3 Matrix{Float64}:\n", + " -0.649854 0.735055 0.193349\n", + " -0.624454 -0.371326 -0.687149\n", + " -0.433297 -0.567284 0.700316\n", + "singular values:\n", + "3-element Vector{Float64}:\n", + " 1.1253009779161582\n", + " 0.27878010490969024\n", + " 0.013851237498536341\n", + "Vt factor:\n", + "3×3 Matrix{Float64}:\n", + " -0.770554 -0.0587937 -0.634658\n", + " 0.636347 -0.0144316 -0.771268\n", + " 0.0361865 -0.998166 0.0485336" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "U, S, V = svd(A) #singular decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c6fdc076-bfc8-46e9-97c2-5427f4c49395", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3×3 BitMatrix:\n", + " 1 0 0\n", + " 0 1 0\n", + " 0 0 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3×3 BitMatrix:\n", + " 1 0 0\n", + " 0 1 0\n", + " 0 0 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3×3 BitMatrix:\n", + " 0 0 0\n", + " 0 0 0\n", + " 0 0 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(U'*U .> 0.00001)\n", + "display(V'*V .> 0.00001)\n", + "display(A - (U * Diagonal(S) * V') .> 0.00001)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "37b7a415-d7ea-4d00-b704-7d79e562d899", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-element Vector{Float64}:\n", + " 0.019506810588402184\n", + " 0.2433655332706062\n", + " 0.9153216003851385" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3-element Vector{Float64}:\n", + " 1.1253009779161582\n", + " 0.27878010490969024\n", + " 0.013851237498536341" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(eigvals(A))\n", + "display(svd(A).S)\n", + "# singular values are more spread out" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "5a4893c6-074c-4479-ac8a-9ad8db295d0a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3, 3), (3,), (5, 3))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "((5, 3), (3,), (3, 3))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# svd exists also for rectangular matrices\n", + "U, S, V = svd(rand(3, 5))\n", + "display((size(U), size(S), size(V)))\n", + "U, S, V = svd(rand(5, 3))\n", + "display((size(U), size(S), size(V)))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "380565b8-f15d-4f3f-9a0f-85fc82809bdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-element Vector{Float64}:\n", + " 2.302775637731995\n", + " 1.3027756377319948\n", + " 0.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svdvals([2 0 1;0 1 1; 0 0 0; 0 0 0]) # rank is 2 -> so 2 non zero values" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "deb05175-6a64-4c40-ab5c-e614ef85797f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Eigen{Float64, Float64, Matrix{Float64}, Vector{Float64}}\n", + "values:\n", + "3-element Vector{Float64}:\n", + " 4.440892098500626e-15\n", + " 1.697224362268007\n", + " 5.302775637731994\n", + "vectors:\n", + "3×3 Matrix{Float64}:\n", + " -0.333333 0.444872 -0.831251\n", + " -0.666667 -0.734656 -0.125841\n", + " 0.666667 -0.51222 -0.541467" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3×3 Matrix{Float64}:\n", + " 4.0 0.0 2.0\n", + " 0.0 1.0 1.0\n", + " 2.0 1.0 2.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A = [2 0 1;0 1 1; 0 0 0];\n", + "display(eigen(A' * A))\n", + "U, S, V = svd(A);\n", + "display(V * Diagonal(S)' * Diagonal(S) * V')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "60516aff-16aa-41e6-a56d-6cad04547e55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3×3 Matrix{Float64}:\n", + " 1.0 -1.0 0.0\n", + " 0.0 1.0 0.0\n", + " 0.0 0.0 0.5" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3×3 Matrix{Float64}:\n", + " 1.0 -1.0 0.0\n", + " -1.11022e-16 1.0 0.0\n", + " 0.0 0.0 0.5" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A = [1 1 0; 0 1 0; 0 0 2];\n", + "display(inv(A))\n", + "F = svd(A);\n", + "display(F.V * Diagonal(F.S .^ -1) * F.U') # we can get the inverse from the svd" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "cd9e606f-957d-4803-9ab8-f7b23c455e13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.0, false)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\"frobenius norm: 2.6457513110645907\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display((opnorm(A), opnorm(A) == norm(A))) # use opnorm for the matrix norm\n", + "display(\"frobenius norm: $(norm(A))\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "9c469ac2-4d2e-4eba-9c6e-c63ec1fc06ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2×2 Matrix{Float64}:\n", + " 1.27357 1.80721\n", + " 2.87898 4.08529" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Eckhart-Young theorem\n", + "# the min X with respect to opnorm(A-X) with rank less than or equal to k is:\n", + "A = [1 2;3 4];\n", + "F = svd(A);\n", + "X = F.U[:,1] .* F.S[1, 1] * F.V[:, 1]';\n", + "display(X)\n", + "display(rank(X))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-04/optQf.m b/10-04/optQf.m new file mode 100644 index 0000000..2aa30be --- /dev/null +++ b/10-04/optQf.m @@ -0,0 +1,67 @@ +function v = optQf( Q , q ) + +%function v = optQf( Q , q ) +% +% Given the data structure encoding a Quadratic function +% +% f( x ) = (1/2) x^T * Q * x + q * x +% +% returns the value of f() in the "putative minimum" (or maximum), i.e., +% the point +% +% xStar = Q \ -q; +% +% Input: +% +% - Q ([ n x n ] real symmetric matrix, not necessarily positive +% semidefinite): the quadratic part of f +% +% - q ([ n x 1 ] real column vector): the linear part of f +% +% Output: +% +% - f( xStar ) +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 30-09-22 + Version 0.10 + Copyright Antonio Frangioni + ======================================= +%} + +if ~ isreal( Q ) + error( 'Q not a real matrix' ); +end + +n = size( Q , 1 ); + +if n <= 1 + error( 'Q is too small' ); +end + +if n ~= size( Q , 2 ) + error( 'Q is not square' ); +end + +if ~ isreal( q ) + error( 'q not a real vector' ); +end + +if size( q , 2 ) ~= 1 + error( 'q is not a (column) vector' ); +end + +if size( q , 1 ) ~= n + error( 'q size does not match with Q' ); +end + +% "solve the problem" - - - - - - - - - - - - - - - - - - - - - - - - - - - +% ignore if Q is singular + +xStar = Q \ -q; + +v = 0.5 * xStar' * Q * xStar + q' * xStar; + +end diff --git a/10-04/plotQ.jl b/10-04/plotQ.jl new file mode 100644 index 0000000..def2207 --- /dev/null +++ b/10-04/plotQ.jl @@ -0,0 +1,11 @@ +using LinearAlgebra +using Plots + +function plotQ(Q::Matrix, q::Vector, xyrange::Tuple{Tuple{Number, Number}, Tuple{Number, Number}})::Plots.Plot + f(x, y) = dot((1/2) * hcat(x, y) * Q, vcat(x, y)) + dot(q', vcat(x, y)) + xrange = LinRange(xyrange[1][1], xyrange[2][1], 200) + yrange = LinRange(xyrange[1][2], xyrange[2][2], 200) + z = @. f(xrange', yrange) + plt = contour(xrange, yrange, z) + plt +end \ No newline at end of file diff --git a/10-04/plotQ.m b/10-04/plotQ.m new file mode 100644 index 0000000..ddb0b38 --- /dev/null +++ b/10-04/plotQ.m @@ -0,0 +1,11 @@ +function plotQ( Q , q , range ) + +f = @(x,y) 0.5 * [ x y ] * Q * [ x ; y ] + q' * [ x ; y ]; + +warning( 'off' , 'all' ); + +fcontour( f , range , 'LineColor' , 'k' , 'LineWidth' , 1 ); + +warning( 'on' , 'all' ); + +end diff --git a/10-12/DIS.jl b/10-12/DIS.jl new file mode 100644 index 0000000..e1f04e9 --- /dev/null +++ b/10-12/DIS.jl @@ -0,0 +1,223 @@ +using LinearAlgebra +using Printf +using Plots + +function DIS(f; + rangeplt::Union{Nothing, Tuple{Real, Real}}=nothing, + sfgrd::Real=0, + eps::Real=1e-6, + MaxFeval::Integer=100, + plt::Union{Plots.Plot, Nothing}=nothing, + plotatend::Bool=true, + Plotg::Integer=0 + )::Tuple{Real, String} + + # Plotg + # 1 = the function value / gap are plotted + # 2 = the function and the model (if used) are plotted + # all the rest: nothing is plotted + + resolution = 1000 + + Interactive = false # if we pause at every iteration + + # reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if rangeplt == nothing + (fStar, _, _, rangeplt) = f(nothing) + else + fStar = -Inf + end + + xm = rangeplt[1] # x_- + xp = rangeplt[2] # x_+ + + if xm > xp + throw(ArgumentError("rangeplt is empty")) + return (NaN, "empty") + end + + (fxm, f1xm, _) = f(xm) + if( f1xm > 0 ) + throw(DomainError(rangeplt, "f'(x_-) must be ≤ 0")) + return (NaN, "empty") + end + + (fxp, f1xp, _) = f(xp) + if( f1xp < 0 ) + throw(DomainError(rangeplt, "f'(x_+) must be ≥ 0")) + return (NaN, "empty") + end + + if (sfgrd < 0) || (sfgrd ≥ 0.5) + throw(DomainError(sfgrd, "sfgrd must be in [ 0 , 1/2 )")) + return (NaN, "empty") + end + + if MaxFeval < 2 + throw(ArgumentError("At least two function computations are required")) + return (NaN, "empty") + end + + # initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + feval = 2 + status = "optimal" + + if f1xm ≥ -eps + return (xm, status) + end + + if f1xp ≤ eps + return (xp, status) + end + + fbest = min(fxp, fxm) + f1x = min(-f1xp, f1xm) + + if Plotg == 1 + gap = [] + end + + if Plotg == 1 && plt == nothing + plt = plot(yscale = :log, + xlims=(0, 35), + ylims=(1e-15, Inf), + guidefontsize=16) + elseif Plotg == 2 && plt == nothing + plt = plot(legend = false) + end + + if iszero(sfgrd) + println("Dichotomic search") + else + @printf("Dichotomic search with safeguarded interpolation (%1.4f)\n", sfgrd) + end + + if fStar > -Inf + println("feval\trel gap\t\tx_-\t\tx_+\t\tx\t\tf'(x)") + else + println("feval\tfbest\t\tx_-\t\tx_+\t\tx\t\tf'(x)") + end + + # main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + x = 0 + + while true + # stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + if feval > MaxFeval + status = "stopped" + break + end + + # main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + if iszero(sfgrd) + # compute the new point as the middle of the inteval + x = (xm + xp) / 2 + else + # compute the new point by safeguarded quadratic interpolation + sfxp = xp - (xp - xm) * sfgrd + sfxm = xm + (xp - xm) * sfgrd + x = (xm * f1xp - xp * f1xm) / (f1xp - f1xm) + x = max(sfxm, min(sfxp, x)) + end + + (fx, f1x, _) = f(x) # compute f(x) and f'(x) + + if fx < fbest + fbest = fx + end + + feval += 1 + + # output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fStar > -Inf + gapk = (fbest - fStar) / max(abs(fStar), 1) + + if Plotg == 1 + push!(gap, gapk) + end + else + gapk = fbest + end + + @printf("%4d\t%1.4e\t%1.8e\t%1.8e\t%1.8e\t%1.4e\n", feval, gapk, xm, xp, x, f1x) + + if Plotg == 2 + xmp = xm - (xp - xm) / 20 + xpp = xp + (xp - xm) / 20 + + for e in plt.series_list + e[:linealpha] = 0 + end + + xx = range(xmp, xpp, resolution) + yy = map(v -> v[1], f.(xx)) + + xlims!(plt, (xmp, xpp)) + old_ylims = ylims(plt) + ylims!(plt, (old_ylims[1], max(fxm, fxp))) + plot!(plt, xx, yy) + + if !iszero(sfgrd) + a = (f1xp - f1xm) / (2 * (xp - xm)) + b = (xp * f1xm - xm * f1xp) / (xp - xm) + # a xm^2 + b xm + c == fxm ==> + # c == fxm - a xm^2 - b xm + c = fxm - a * xm^2 - b * xm + + xlims!(plt, (xmp, xpp)) + new_xticks = ([xmp, xm, sfxm, sfxp, xp, xpp], + [@sprintf("%1.1g", xmp), "x-", "sx-", "sx+", "x+", @sprintf("%1.1g", xpp)]) + + xticks!(plt, new_xticks) + plot!(plt, xx, @. a*xx^2 + b*xx + c) + else + new_xticks = ([xmp, xm, x, xp, xpp], + [@sprintf("%1.1g", xmp), "x-", "x", "x+", @sprintf("%1.1g", xpp)]) + + xticks!(plt, new_xticks) + end + end + # stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + if abs(f1x) ≤ eps + break + end + + # restrict the interval based on sign of the derivative in xn - - - - - + if f1x < 0 + xm = x + fxm = fx + f1xm = f1x + else + xp = x + fxp = fx + f1xp = f1x + end + + if Plotg ≠ 0 && Interactive + IJulia.clear_output(wait=true) + display(plt) + sleep(0.1) + readline() + end + + # iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + end + + if Plotg == 1 + plot!(plt, + gap, + linewidth=2) + end + + if plotatend && Plotg ≠ 0 + display(plt) + end + + (x, status) +end \ No newline at end of file diff --git a/10-12/DIS.m b/10-12/DIS.m new file mode 100644 index 0000000..b3c6d18 --- /dev/null +++ b/10-12/DIS.m @@ -0,0 +1,325 @@ +function [ x , status ] = DIS( f , varargin ) + +%function [ x , status ] = DIS( f , range , sfgrd , delta , MaxFeval ) +% +% Apply the classical Dichotomic Search for the minimization of the +% provided one-dimensional function f, which must have the following +% interface: +% +% [ v , varargout ] = f( x ) +% +% Input: +% +% - x is either a scalar real denoting the input of f(), or [] (empty). +% +% Output: +% +% - v (real, scalar): if x == [] this is the best known lower bound on +% the global optimum of f() on the standard interval in which f() is +% supposed to be minimised (see next). If x ~= [] then v = f(x). +% +% - g (real, either scalar or a [ 2 x 1 ] matrix denoting an interval) is +% the first optional argument. This also depends on x. if x == [] then +% g is a [ 2 x 1 ] matrix denoting the standard interval in which f() +% is supposed to be minimised (into which v is the minimum). f() is +% never called with x ~= []. +% +% IMPORTANT NOTE: the function requires f() to be able to provide the first +% derivative, and it requires that the interval +% [ x_- , x_+ ] is chosen such that f'( x_- ) <= 0 and f'( x_+ ) >= 0. +% +% The Dichotomic Search can either be "blind" (new point right in the +% middle of the interval) or using a safeguarded quadratic Interpolation +% to choose it, as dictated by the other [optional] input parameters: +% +% - range: (either [ 2 x 1 ] real vector or [], default []): the range +% in which the local minimum has to be seached; if range == [], the +% default range point provided by f() is used. +% +% - sfgrd (real scalar, default value 0): if sfgrd == 0, the Dichotomic +% Search is "blind", i .e., the new point is always chosen right in the +% middle of the current interval. Otherwise, it must be 0 < sfgrd < 0.5 +% and a safeguarded quadratic Interpolation technique is used to choose +% it, where it is guaranteed that at least ( 1 - sfgrd ) of the current +% interval will be discarded. +% +% - eps (real scalar, default value 1e-6): the accuracy in the stopping +% criterion: the algorithm is stopped when a point is found such that +% the absolute value of the derivative is less than or equal to eps. +% +% - MaxFeval (integer scalar, default value 100): the maximum number of +% function evaluations (hence, iterations will be not more than +% MaxFeval - 2 because at each iteration one function evaluation is +% performed, except in the first one when two are). +% +% Output: +% +% - x (real scalar): the best solution found so far. +% +% - status (string): a string describing the status of the algorithm at +% termination +% +% = 'optimal': the algorithm terminated having proven that x is a(n +% approximately) optimal solution, i.e., the diameter of the +% restricted range is less than or equal to delta. +% +% = 'empty': the provided range is empty (x_- > x_+) and therefore +% such is the optimization problem +% +% = 'stopped': the algorithm terminated having exhausted the maximum +% number of iterations: x is the best solution found so far, but not +% necessarily the optimal one +% +% = 'error': the algorithm found a numerical error that prevents it from +% continuing optimization +% +% TODO: implement a warm-op phase whereby if f'( x_- ) > 0 then x_- is +% "quickly moved left" until the derivative is negative, and, +% symmetrically, if f'( x_+ ) < 0 then it is "quickly moved right". +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 09-08-21 + Version 0.10 + Copyright Antonio Frangioni + ======================================= +%} + +Plotg = 1; +% 1 = the function value / gap are plotted +% 2 = the function and the model (if used) are plotted +% all the rest: nothing is plotted + +Interactive = true; % if we pause at every iteration + +% reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +if ~ isa( f , 'function_handle' ) + error( 'f not a function' ); +end + +if isempty( varargin ) || isempty( varargin{ 1 } ) + [ fStar , range ] = f( [] ); +else + fStar = - Inf; % if the range is not the standard one, we can't trust + % the standard global minima + range = varargin{ 1 }; +end + +if ~ isreal( range ) + error( 'range not a real vector' ); +end + +if ( size( range , 1 ) ~= 1 ) || ( size( range , 2 ) ~= 2 ) + error( 'range is not a [ 1 x 2 ] vector' ); +end + +xm = range( 1 ); % x_- +xp = range( 2 ); % x_+ + +if xm > xp + fprintf( 'range is empty\n' ); + x = NaN; + status = 'empty'; + return; +end + +[ fxm , f1xm ] = f( xm ); +if( f1xm > 0 ) + error( 'f''( x_- ) must be <= 0' ); +end + +[ fxp , f1xp ] = f( xp ); +if( f1xp < 0 ) + error( 'f''( x_+ ) must be >= 0' ); +end + +if length( varargin ) > 1 + sfgrd = varargin{ 2 }; + if ~ isreal( sfgrd ) || ~ isscalar( sfgrd ) + error( 'sfgrd is not a real scalar' ); + end + if ( sfgrd < 0 ) || ( sfgrd >= 0.5 ) + error( 'sfgrd must be in [ 0 , 1/2 )' ); + end +else + sfgrd = 0; +end + +if length( varargin ) > 2 + eps = varargin{ 3 }; + if ~ isreal( eps ) || ~ isscalar( eps ) + error( 'eps is not a real scalar' ); + end +else + eps = 1e-6; +end + +if length( varargin ) > 3 + MaxFeval = round( varargin{ 4 } ); + if ~ isscalar( MaxFeval ) + error( 'MaxFeval is not an integer scalar' ); + end +else + MaxFeval = 100; +end + +% initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +feval = 2; + +status = 'optimal'; + +if f1xm >= - eps + x = xm; + return; +end + +if f1xp <= eps + x = xp; + return; +end + +fbest = min( [ fxp fxm ] ); +f1x = min( [ -f1xp f1xm ] ); + +if Plotg == 1 + gap = []; +end + +if sfgrd == 0 + fprintf( 'Dichotomic search\n'); +else + fprintf( ... + 'Dichotomic search with safeguarded interpolation (%1.4f)\n' , sfgrd ); +end +if fStar > - Inf + fprintf( 'feval\trel gap\t\tx_-\t\tx_+\t\tx\t\tf''(x)\n'); +else + fprintf( 'feval\tfbest\t\tx_-\t\tx_+\t\tx\t\tf''(x)\n'); +end + +% main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +while true + + % stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + + if feval > MaxFeval + status = 'stopped'; + break; + end + + % main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if sfgrd == 0 + % compute the new point as the middle of the inteval + x = ( xm + xp ) / 2; + else + % compute the new point by safeguarded quadratic interpolation + sfxp = xp - ( xp - xm ) * sfgrd; + sfxm = xm + ( xp - xm ) * sfgrd; + x = ( xm * f1xp - xp * f1xm ) / ( f1xp - f1xm ); + x = max( [ sfxm min( [ sfxp x ] ) ] ); + end + + [ fx , f1x ] = f( x ); % compute f( x ) and f'( x ) + + if fx < fbest + fbest = fx; + end + + feval = feval + 1; + + % output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fStar > - Inf + gapk = ( fbest - fStar ) / max( [ abs( fStar ) 1 ] ); + + if Plotg == 1 + gap( end + 1 ) = gapk; + semilogy( gap , 'Color' , 'k' , 'LineWidth' , 2 ); + xlim( [ 0 35 ] ); + ylim( [ 1e-15 inf ] ); + ax = gca; + ax.FontSize = 16; + ax.Position = [ 0.03 0.07 0.95 0.92 ]; + ax.Toolbar.Visible = 'off'; + end + else + gapk = fbest; + end + fprintf( '%4d\t%1.4e\t%1.8e\t%1.8e\t%1.8e\t%1.4e\n' , feval , ... + gapk , xm , xp , x , f1x ); + + if Plotg == 2 + xmp = xm - ( xp - xm ) / 20; + xpp = xp + ( xp - xm ) / 20; + + warning( 'off' , 'all' ); + fplot( @(x) f( x ) , [ xmp xpp ] , 'Color' , 'k' , ... + 'LineWidth' , 1 ); + + xlim( [ xmp xpp ] ); + yticks( [] ); + ax = gca; + ax.FontSize = 16; + ax.Toolbar.Visible = 'off'; + ax.Position = [ 0.025 0.05 0.95 0.95 ]; + if sfgrd ~= 0 + hold on; + a = ( f1xp - f1xm ) / ( 2 * ( xp - xm ) ); + b = ( xp * f1xm - xm * f1xp ) / ( xp - xm ); + % a xm^2 + b xm + c == fxm ==> + % c == fxm - a xm^2 - b xm + c = fxm - a * xm^2 - b * xm; + fplot( @(x) a * x^2 + b * x + c , [ xmp xpp ] , ... + 'Color' , 'b' , 'LineWidth' , 1 ); + xticks( [ xmp xm sfxm sfxp xp xpp ] ); + xticklabels( { num2str( xmp , '%1.1g' ) , 'x-' , 'sx-' , ... + 'sx+' , 'x+' , num2str( xpp , '%1.1g' ) } ); + else + xticks( [ xmp xm x xp xpp ] ); + xticklabels( { num2str( xmp , '%1.1g' ) , 'x-' , 'x' , ... + 'x+' , num2str( xpp , '%1.1g' ) } ); + end + warning( 'on' , 'all' ); + hold off; + end + + % check stopping condition- - - - - - - - - - - - - - - - - - - - - - - + + if abs( f1x ) <= eps + break; + end + + % restrict the interval based on sign of the derivative in xn - - - - - + if f1x < 0 + xm = x; + fxm = fx; + f1xm = f1x; + else + xp = x; + fxp = fx; + f1xp = f1x; + end + + if Interactive + pause; + end + + % iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end + +% end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end % the end- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/10-12/GRS.jl b/10-12/GRS.jl new file mode 100644 index 0000000..d82c9d6 --- /dev/null +++ b/10-12/GRS.jl @@ -0,0 +1,187 @@ +using Plots +using LinearAlgebra +using Printf + +function GRS(f; + rangeplt::Union{Nothing, Tuple{Real, Real}}=nothing, + delta::Real=1e-6, + MaxFeval::Integer=100, + plt::Union{Plots.Plot, Nothing}=nothing, + plotatend::Bool=true, + Plotg::Integer=0 + )::Tuple{Real, String} + + # Plotg + # 0 = nothing is plotted + # 1 = the function value / gap are plotted + # 2 = the function and the test points used are plotted + + resolution = 1000 + + Interactive = false # if we pause at every iteration + + # reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if rangeplt == nothing + (fStar, _, _, rangeplt) = f(nothing) + else + fStar = -Inf + end + + xm = rangeplt[1] # x_- + xp = rangeplt[2] # x_+ + + if xm > xp + throw(ArgumentError("rangeplt is empty")) + return (NaN, "empty") + end + + if MaxFeval < 2 + throw(ArgumentError("At least two function computations are required")) + return (NaN, "empty") + end + + # initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + r = (sqrt( 5 ) - 1)/2 + + xmp = xm + (1 - r) * (xp - xm) # x'_- + xpp = xm + r * (xp - xm) # x'_+ + + fxmp = f(xmp)[1] # f(x'_-) + fxpp = f(xpp)[1] # f(x'_+) + + feval = 2 + + if fxmp ≤ fxpp + fx = fxmp + else + fx = fxpp + end + + status = "optimal" + + if Plotg > 0 + gap = [] + end + + if Plotg == 1 && plt == nothing + plt = plot(yscale = :log, + xlims=(0, 35), + ylims=(1e-15, Inf), + guidefontsize=16) + elseif Plotg == 2 && plt == nothing + plt = plot(legend = false) + end + + print("Golden ratio search\n") + if fStar > -Inf + print("feval\trel gap\t\tx_-\t\tx_+\n") + else + print("feval\tfbest\t\tx_-\t\tx_+\n") + end + + # main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + while xp - xm > delta + + # output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fStar > -Inf + gapk = (fx - fStar) / max(abs(fStar), 1) + + if Plotg == 1 + push!(gap, gapk) + end + else + gapk = fx + end + @printf("%4d\t%1.4e\t%1.8e\t%1.8e\n", feval, gapk, xm, xp) + + if Plotg == 2 + xbot = xm - ( xp - xm ) / 20; + xtop = xp + ( xp - xm ) / 20; + + if !isempty(plt.series_list) + plt.series_list[end][:linealpha] = 0 + end + xx = range(xbot, xtop, resolution) + yy = map(v -> v[1], f.(xx)) + xlims!(plt, (xbot, xtop)) + old_ylims = ylims(plt) + ylims!(plt, (old_ylims[1], max(fxmp, fxpp))) + plot!(plt, xx, yy) + + # old_xticks = xticks(plt[1]) + new_xticks = ([xbot, xm, xmp, xpp, xp, xtop], + [@sprintf("%1.1g", xbot), "x-", "x''-", "x''+", "x+", @sprintf("%1.1g", xtop)]) + # keep_indices = findall(x -> all(x .≠ new_xticks[1]), old_xticks[1]) + # merged_xticks = (old_xticks[1][keep_indices] ∪ new_xticks[1], + # old_xticks[2][keep_indices] ∪ new_xticks[2]) + # xticks!(merged_xticks) + xticks!(plt, new_xticks) + end + + # stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + if feval > MaxFeval + status = "stopped" + break + end + + # main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fxmp <= fxpp + xp = xpp + xpp = xmp + xmp = xm + (1 - r) * (xp - xm) + + fxpp = fxmp + fx = fxmp + fxmp = f(xmp)[1] + else + xm = xmp + xmp = xpp + xpp = xm + r * (xp - xm) + + fxmp = fxpp + fx = fxpp + fxpp = f(xpp)[1] + end + + feval += 1 + + if Plotg ≠ 0 && Interactive + IJulia.clear_output(wait=true) + display(plt) + sleep(0.1) + readline() + end + + # iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + end + + # end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + # select final answer + if fxmp ≤ fxpp + x = xmp + else + x = xpp + end + + if Plotg == 1 + plot!(plt, + gap, + linewidth=2) + end + + if plotatend && Plotg ≠ 0 + display(plt) + end + + (x, status) +end \ No newline at end of file diff --git a/10-12/GRS.m b/10-12/GRS.m new file mode 100644 index 0000000..b5d0658 --- /dev/null +++ b/10-12/GRS.m @@ -0,0 +1,252 @@ +function [ x , status ] = GRS( f , varargin ) + +%function [ x , status ] = GRS( f , range , delta , MaxFeval ) +% +% Apply the classical Golden Ratio Search for the minimization of the +% provided one-dimensional function f, which must have the following +% interface: +% +% [ v , varargout ] = f( x ) +% +% Input: +% +% - x is either a scalar real denoting the input of f(), or [] (empty). +% +% Output: +% +% - v (real, scalar): if x == [] this is the best known lower bound on +% the global optimum of f() on the standard interval in which f() is +% supposed to be minimised (see next). If x ~= [] then v = f(x). +% +% - g (real, either scalar or a [ 2 x 1 ] matrix denoting an interval) is +% the first optional argument. This also depends on x. if x == [] then +% g is a [ 2 x 1 ] matrix denoting the standard interval in which f() +% is supposed to be minimised (into which v is the minimum). f() is +% never called with x ~= []. +% +% The other [optional] input parameters are: +% +% - range: (either [ 2 x 1 ] real vector or [], default []): the range +% in which the local minimum has to be seached; if range == [], the +% default range point provided by f() is used. +% +% - delta (real scalar, default value 1e-6): the accuracy in the stopping +% criterion: the algorithm is stopped when the diameter of the +% restricted range is less than or equal to delta. +% +% - MaxFeval (integer scalar, default value 100): the maximum number of +% function evaluations (hence, iterations will be not more than +% MaxFeval - 2 because at each iteration one function evaluation is +% performed, except in the first one when two are). +% +% Output: +% +% - x (real scalar): the best solution found so far. +% +% - status (string): a string describing the status of the algorithm at +% termination +% +% = 'optimal': the algorithm terminated having proven that x is a(n +% approximately) optimal solution, i.e., the diameter of the +% restricted range is less than or equal to delta. +% +% = 'empty': the provided range is empty (x_- > x_+) and therefore +% such is the optimization problem +% +% = 'stopped': the algorithm terminated having exhausted the maximum +% number of iterations: x is the best solution found so far, but not +% necessarily the optimal one +% +% = 'error': the algorithm found a numerical error that prevents it from +% continuing optimization +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 09-08-21 + Version 0.10 + Copyright Antonio Frangioni + ======================================= +%} + +Plotg = 1; +% 0 = nothing is plotted +% 1 = the function value / gap are plotted +% 2 = the function and the test points used are plotted + +Interactive = true; % if we pause at every iteration + +% reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +if ~ isa( f , 'function_handle' ) + error( 'f not a function' ); +end + +if isempty( varargin ) || isempty( varargin{ 1 } ) + [ fStar , range ] = f( [] ); +else + fStar = - Inf; % if the range is not the standard one, we can't trust + % the standard global minima + range = varargin{ 1 }; +end + +if ~ isreal( range ) + error( 'range not a real vector' ); +end + +if ( size( range , 1 ) ~= 1 ) || ( size( range , 2 ) ~= 2 ) + error( 'range is not a [ 1 x 2 ] vector' ); +end + +xm = range( 1 ); % x_- +xp = range( 2 ); % x_+ + +if xm > xp + fprintf( 'range is empty\n' ); + x = NaN; + status = 'empty'; + return; +end + +if length( varargin ) > 1 + delta = varargin{ 2 }; + if ~ isreal( delta ) || ~ isscalar( delta ) + error( 'delta is not a real scalar' ); + end +else + delta = 1e-6; +end + +if length( varargin ) > 2 + MaxFeval = round( varargin{ 3 } ); + if ~ isscalar( MaxFeval ) + error( 'MaxFeval is not an integer scalar' ); + end + if MaxFeval < 2 + error( 'at least two function computations are required' ); + end +else + MaxFeval = 100; +end + +% initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +r = ( sqrt( 5 ) - 1 ) / 2; + +xmp = xm + ( 1 - r ) * ( xp - xm ); % x'_- +xpp = xm + r * ( xp - xm ); % x'_+ + +fxmp = f( xmp ); % f( x'_- ) +fxpp = f( xpp ); % f( x'_+ ) + +feval = 2; + +if fxmp <= fxpp + fx = fxmp; +else + fx = fxpp; +end + +status = 'optimal'; + +if Plotg + gap = []; +end + +fprintf( 'Golden ratio search\n'); +if fStar > - Inf + fprintf( 'feval\trel gap\t\tx_-\t\tx_+\n'); +else + fprintf( 'feval\tfbest\t\tx_-\t\tx_+\n'); +end + +% main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +while xp - xm > delta + + % output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fStar > - Inf + gapk = ( fx - fStar ) / max( [ abs( fStar ) 1 ] ); + + if Plotg == 1 + gap( end + 1 ) = gapk; + semilogy( gap , 'Color' , 'k' , 'LineWidth' , 2 ); + xlim( [ 0 35 ] ); + ylim( [ 1e-15 inf ] ); + ax = gca; + ax.FontSize = 16; + ax.Position = [ 0.03 0.07 0.95 0.92 ]; + ax.Toolbar.Visible = 'off'; + end + else + gapk = fx; + end + fprintf( '%4d\t%1.4e\t%1.8e\t%1.8e\n' , feval , gapk , xm , xp ); + + if Plotg == 2 + xbot = xm - ( xp - xm ) / 20; + xtop = xp + ( xp - xm ) / 20; + + warning( 'off' , 'all' ); + fplot( @(x) f( x ) , [ xbot xtop ] , 'Color' , 'k' , ... + 'LineWidth' , 1 ); + + xlim( [ xbot xtop ] ); + yticks( [] ); + ax = gca; + ax.FontSize = 16; + ax.Position = [ 0.025 0.05 0.95 0.95 ]; + ax.Toolbar.Visible = 'off'; + xticks( [ xbot xm xmp xpp xp xtop ] ); + xticklabels( { num2str( xbot , '%1.1g' ) , 'x-' , 'x''-' , ... + 'x''+' , 'x+' , num2str( xtop , '%1.1g' ) } ); + warning( 'on' , 'all' ); + end + + + % stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + + if feval > MaxFeval + status = 'stopped'; + break; + end + + % main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fxmp <= fxpp + xp = xpp; xpp = xmp; xmp = xm + ( 1 - r ) * ( xp - xm ); + fxpp = fxmp; fx = fxmp; fxmp = f( xmp ); + else + xm = xmp; xmp = xpp; xpp = xm + r * ( xp - xm ); + fxmp = fxpp; fx = fxpp; fxpp = f( xpp ); + end + + feval = feval + 1; + + if Interactive + pause; + end + + % iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end + +% end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +% select final answer + +if fxmp <= fxpp + x = xmp; +else + x = xpp; +end + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end % the end- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/10-12/UNM.jl b/10-12/UNM.jl new file mode 100644 index 0000000..8fb52dd --- /dev/null +++ b/10-12/UNM.jl @@ -0,0 +1,206 @@ +using LinearAlgebra +using Printf +using Plots +using LaTeXStrings + +function UNM(f; + x::Union{Nothing, Real}=nothing, + eps::Real=1e-6, + finf::Real=1e+8, + MaxFeval::Integer=30, + plt::Union{Plots.Plot, Nothing}=nothing, + plotatend::Bool=true, + Plotg::Integer=0 + )::Tuple{Real, String} + + # Plotg + # 1 = the function value / gap are plotted + # 2 = the function and the second-order model are plotted + # 3 = the function, the first-order model and the second-order model are + # plotted (the first-order model has no role in the algorithm, but + # this shows how much better the second-order model is than the + # first-order one) + + resolution = 1000 + + Interactive = false + + # reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + (fStar, _, _, rangeplt) = f(nothing) + + if x == nothing + x = rangeplt[1] + end + + if finf ≤ 0 + throw(DomainError(finf, "finf must be in > 0")) + end + + if MaxFeval < 2 + throw(ArgumentError("At least two function computations are required")) + return (NaN, "empty") + end + + # initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + feval = 0 + status = "optimal" + fbest = Inf + + if Plotg == 1 + gap = [] + end + + if Plotg == 1 && plt == nothing + plt = plot(yscale = :log, + xlims=(0, 35), + ylims=(1e-15, Inf), + guidefontsize=16) + elseif Plotg == 2 && plt == nothing + plt = plot(legend = false) + elseif Plotg == 3 && plt == nothing + plt = plot(legend = false) + end + + + println("Univariate Newton's Method") + + if fStar > -Inf + println("feval\trel gap\t\tx\t\tf(x)\t\tf'(x)\t\tf''(x)") + else + println("feval\tfbest\t\tx\t\tf'(x)\t\tf'(x)\t\tf''(x)") + end + + # main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + while true + # compute f(x), f'(x), f''(x) - - - - - - - - - - - - - - - - - - - - - + + (fx, f1x, f2x) = f(x) + + feval += 1 + + if fx < fbest + fbest = fx + end + + if abs(f2x) ≤ 1e-16 + status = "stopped" + @printf("numerical issue: f''(x) = %1.4e\n", f2x) + break + end + + # main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + xn = x - f1x/f2x + + # output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fStar > -Inf + gapk = (fbest - fStar) / max(abs(fStar), 1) + + if Plotg == 1 + push!(gap, gapk) + end + else + gapk = fbest + end + + @printf("%4d\t%1.4e\t%1.8e\t%1.4e\t%1.4e\t%1.4e\n", feval, gapk, x, fx, f1x, f2x) + + if Plotg > 1 + xm = min(x, xn) - abs(x - xn) / 5 + xp = max(x, xn) + abs(x - xn) / 5 + + xx = range(xm, xp, resolution) + yy = map(v -> v[1], f.(xx)) + + for e in plt.series_list + e[:linealpha] = 0 + end + + old_ylims = ylims(plt) + ylims!(plt, (minimum(yy), max(yy[1], yy[end]))) + + xlims!(plt, (xm, xp)) + + plot!(plt, xx, yy) + + if Plotg == 3 + # first-order model is + # f( y ) = f( x ) + f'( x )( y - x ) + # = [ f( x ) - f'( x ) x ] + # + f'( x ) y + b = f1x + c = fx - f1x*x + + yy = b .* xx .+ c + plot!(plt, xx, yy) + end + # second-order model is + # f( y ) = f( x ) + f'( x )( y - x ) + f''( x )( y - x )^2 / 2 + # = [ f( x ) - f'( x ) x + f''( x ) x^2 / 2 ] + # + [ f'( x ) - f''( x ) x ] y + # + f''( x )y^2 / 2 + a = f2x/2 + b = f1x - f2x*x + c = fx - f1x*x + f2x*x^2 / 2 + + yy = @. a * xx^2 + b * xx + c + plot!(plt, xx, yy) + + new_xticks = [] + if x < xn + new_xticks = ([xm, x, xn, xp], + [@sprintf("%1.1g", xm), L"x^k", L"x^{k+1}", @sprintf("%1.1g", xp)]) + else + new_xticks = ([xm, xn, x, xp], + [@sprintf("%1.1g", xm), L"x^{k+1}", L"x^k", @sprintf("%1.1g", xp)]) + end + xticks!(plt, new_xticks) + end + + # stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + + if abs(f1x) ≤ eps + break + end + + if feval > MaxFeval + status = "stopped" + break + end + + if abs(fx) > finf + status = "error" + break + end + + if Plotg ≠ 0 && Interactive + IJulia.clear_output(wait=true) + display(plt) + sleep(0.1) + readline() + end + + # iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + x = xn + end + + if Plotg == 1 + plot!(plt, + gap, + linewidth=2) + end + + if plotatend && Plotg ≠ 0 + display(plt) + end + + (x, status) +end \ No newline at end of file diff --git a/10-12/UNM.m b/10-12/UNM.m new file mode 100644 index 0000000..5580d55 --- /dev/null +++ b/10-12/UNM.m @@ -0,0 +1,290 @@ +function [ x , status ] = UNM( f , varargin ) + +%function [ x , status ] = UNM( f , x , eps , finf , MaxFeval ) +% +% Apply the pure, non-globalised Newton Method for the minimization (or +% maximization, since the method does not distinguish between the two) of +% the provided one-dimensional (Univariate) function f, which must have +% the following interface: +% +% [ v , varargout ] = f( x ) +% +% Input: +% +% - x is either a scalar real denoting the input of f(), or [] (empty). +% +% Output: +% +% - v (real, scalar): if x == [] this is the best known lower bound on +% the global optimum of f() on the standard interval in which f() is +% supposed to be minimised (see next). If x ~= [] then v = f(x). +% +% - g (real, either scalar or a [ 2 x 1 ] matrix denoting an interval) is +% the first optional argument. This also depends on x. if x == [] then +% g is a [ 2 x 1 ] matrix denoting the standard interval in which f() +% is supposed to be minimised (into which v is the minimum). f() is +% never called with x ~= []. +% +% - H (real, scalar) is the second optional argument. This must only be +% specified if x ~= [], and it is the second derivative h = f''(x). +% If no such information is available, the function throws error. +% +% IMPORTANT NOTE: the function requires f() to be able to provide both the +% first and the second derivative. +% +% The other [optional] input parameters: +% +% - x: (either a real scalar or [], default []): the starting point; if +% x == [], the left extreme of default range point provided by f() is +% used. +% +% - eps (real scalar, default value 1e-6): the accuracy in the stopping +% criterion: the algorithm is stopped when a point is found such that +% the absolute value of the derivative is less than or equal to eps. +% +% - finf (real scalar, default value 1e+8): since the non-globalised +% Newton Method may diverge, a very rough divergence test is +% implemented whereby if | f( x ) | >= finf then the algorithm is +% stopped with an error condition. +% +% - MaxFeval (integer scalar, default value 30): the maximum number of +% function evaluations (hence, iterations will be not more than +% MaxFeval - 2 because at each iteration one function evaluation is +% performed, except in the first one when two are). +% +% Output: +% +% - x (real scalar): the best solution found so far. +% +% - status (string): a string describing the status of the algorithm at +% termination +% +% = 'optimal': the algorithm terminated having proven that x is a(n +% approximately) optimal solution, i.e., the diameter of the +% restricted range is less than or equal to delta. +% +% = 'stopped': the algorithm terminated having exhausted the maximum +% number of iterations: x is the best solution found so far, but not +% necessarily the optimal one +% +% = 'error': the algorithm found a numerical error that prevents it from +% continuing optimization, such as finding f''( x ) very close to 0, +% or it is found to be diverging (see finf above). +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 29-09-21 + Version 0.20 + Copyright Antonio Frangioni + ======================================= +%} + +Plotg = 3; +% 1 = the function value / gap are plotted +% 2 = the function and the second-order model are plotted +% 3 = the function, the first-order model and the second-order model are +% plotted (the first-order model has no role in the algorithm, but +% this shows how much better the second-order model is than the +% first-order one) +% all the rest: nothing is plotted + +Interactive = true; % if we pause at every iteration + +% reading and checking input- - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +if ~ isa( f , 'function_handle' ) + error( 'f not a function' ); +end + +[ fStar , range ] = f( [] ); + +if isempty( varargin ) || isempty( varargin{ 1 } ) + x = range( 1 ); +else + x = varargin{ 1 }; + + if ~ isreal( x ) || ~ isscalar( x ) + error( 'x not a real scakar' ); + end +end + +if length( varargin ) > 1 + eps = varargin{ 2 }; + if ~ isreal( eps ) || ~ isscalar( eps ) + error( 'eps is not a real scalar' ); + end +else + eps = 1e-6; +end + +if length( varargin ) > 2 + finf = varargin{ 3 }; + if ~ isreal( finf ) || ~ isscalar( finf ) + error( 'finf is not a real scalar' ); + end + if finf <= 0 + error( 'finf must be in > 0' ); + end +else + finf = 1e+8; +end + +if length( varargin ) > 3 + MaxFeval = round( varargin{ 4 } ); + if ~ isscalar( MaxFeval ) + error( 'MaxFeval is not an integer scalar' ); + end +else + MaxFeval = 30; +end + +% initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +feval = 0; + +status = 'optimal'; + +fbest = Inf; + +if Plotg == 1 + gap = []; +end + +fprintf( 'Univariate Newton''s Method\n'); +if fStar > - Inf + fprintf( 'feval\trel gap\t\tx\t\tf(x)\t\tf''(x)\t\tf''''(x)\n'); +else + fprintf( 'feval\tfbest\t\tx\t\tf''(x)\t\tf''(x)\t\tf''''(x)\n'); +end + +% main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +while true + + % compute f( x ), f'( x ), f''( x ) - - - - - - - - - - - - - - - - - - + + [ fx , f1x , f2x ] = f( x ); + + feval = feval + 1; + + if fx < fbest + fbest = fx; + end + + if abs( f2x ) <= 1e-16 + status = 'stopped'; + fprintf( 'numerical issue: f''''(x) = %1.4e\n' , f2x ); + break; + end + + % main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + xn = x - f1x / f2x; + + [ fx , f1x ] = f( x ); % compute f( x ) and f'( x ) + + % output statistics - - - - - - - - - - - - - - - - - - - - - - - - - - + + if fStar > - Inf + gapk = ( fbest - fStar ) / max( [ abs( fStar ) 1 ] ); + + if Plotg == 1 + gap( end + 1 ) = gapk; + semilogy( gap , 'Color' , 'k' , 'LineWidth' , 2 ); + xlim( [ 0 35 ] ); + ylim( [ 1e-15 inf ] ); + ax = gca; + ax.FontSize = 16; + ax.Position = [ 0.03 0.07 0.95 0.92 ]; + ax.Toolbar.Visible = 'off'; + end + else + gapk = fbest; + end + + fprintf( '%4d\t%1.4e\t%1.8e\t%1.4e\t%1.4e\t%1.4e\n' , feval , ... + gapk , x , fx , f1x , f2x ); + + if Plotg > 1 + xm = min( [ x xn ] ) - abs( x - xn ) / 5; + xp = max( [ x xn ] ) + abs( x - xn ) / 5; + + warning( 'off' , 'all' ); + fplot( @(x) f( x ) , [ xm xp ] , 'Color' , 'k' , 'LineWidth' , 1 ); + + xlim( [ xm xp ] ); + yticks( [] ); + ax = gca; + ax.FontSize = 16; + ax.Toolbar.Visible = 'off'; + + hold on; + if Plotg == 3 + % first-order model is + % f( y ) = f( x ) + f'( x )( y - x ) + % = [ f( x ) - f'( x ) x ] + % + f'( x ) y + b = f1x; + c = fx - f1x * x; + fplot( @(x) b * x + c , [ xm xp ] , ... + 'Color' , 'r' , 'LineWidth' , 1 ); + end + % second-order model is + % f( y ) = f( x ) + f'( x )( y - x ) + f''( x )( y - x )^2 / 2 + % = [ f( x ) - f'( x ) x + f''( x ) x^2 / 2 ] + % + [ f'( x ) - f''( x ) x ] y + % + f''( x )y^2 / 2 + a = f2x / 2; + b = f1x - f2x * x; + c = fx - f1x * x + f2x * x^2 / 2; + fplot( @(x) a * x^2 + b * x + c , [ xm xp ] , ... + 'Color' , 'b' , 'LineWidth' , 1 ); + if x < xn + xticks( [ xm x xn xp ] ); + xticklabels( { num2str( xm , '%1.1g' ) , 'x^k' , ... + 'x^{k+1}' , num2str( xp , '%1.1g' ) } ); + else + xticks( [ xm xn x xp ] ); + xticklabels( { num2str( xm , '%1.1g' ) , 'x^{k+1}' , ... + 'x^k' , num2str( xp , '%1.1g' ) } ); + end + warning( 'on' , 'all' ); + hold off; + end + + % stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - - + + if abs( f1x ) <= eps + break; + end + + if feval > MaxFeval + status = 'stopped'; + break; + end + + if abs( fx ) > finf + status = 'error'; + break; + end + + if Interactive + pause; + end + + % iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + x = xn; +end + +% end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end % the end- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/10-12/lesson.ipynb b/10-12/lesson.ipynb new file mode 100644 index 0000000..232ba27 --- /dev/null +++ b/10-12/lesson.ipynb @@ -0,0 +1,1315 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cb977d1b-5910-4d86-bf69-a0e171cb2fd5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\frac{9 x^{9}}{64} - \\frac{117 x^{8}}{74} + \\frac{576 x^{7}}{91} - \\frac{847 x^{6}}{93} - \\frac{23 x^{5}}{6} + \\frac{465 x^{4}}{23} - \\frac{216 x^{3}}{25} - \\frac{19 x^{2}}{2} + \\frac{91 x}{15}$" + ], + "text/plain": [ + " 9 8 7 6 5 4 3 2 \n", + "9⋅x 117⋅x 576⋅x 847⋅x 23⋅x 465⋅x 216⋅x 19⋅x 91⋅x\n", + "──── - ────── + ────── - ────── - ───── + ────── - ────── - ───── + ────\n", + " 64 74 91 93 6 23 25 2 15 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\frac{81 x^{8}}{64} - \\frac{468 x^{7}}{37} + \\frac{576 x^{6}}{13} - \\frac{1694 x^{5}}{31} - \\frac{115 x^{4}}{6} + \\frac{1860 x^{3}}{23} - \\frac{648 x^{2}}{25} - 19 x + \\frac{91}{15}$" + ], + "text/plain": [ + " 8 7 6 5 4 3 2 \n", + "81⋅x 468⋅x 576⋅x 1694⋅x 115⋅x 1860⋅x 648⋅x 91\n", + "───── - ────── + ────── - ─────── - ────── + ─────── - ────── - 19⋅x + ──\n", + " 64 37 13 31 6 23 25 15" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "using SymPy\n", + "x = Sym(\"x\")\n", + "f(x) = 91 * x^2/30 - 19 * x^3 / 6 - 54 * x^4 / 25 + 93 * x^5 / 23 - 23 * x^6 / 36 - 121 * x^7 / 93 + 72 * x^8 / 91 - 13 * x^9 / 74 + 9 * x^10 / 640\n", + "\n", + "Dx = Differential(x)\n", + "\n", + "Dx(f(x)) |> display\n", + "Dx(Dx(f(x))) |> display\n", + "\n", + "f(0) |> display" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23020315-a21b-40e9-b438-c2ea4c6906fa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using Plots\n", + "using LaTeXStrings\n", + "\n", + "include(\"oneDTestFunctions.jl\")\n", + "\n", + "x = range(-1, 3, length=1000)\n", + "y1 = map(v -> v[1], oneDTestFunctions()[1].(x))\n", + "y2 = map(v -> v[2], oneDTestFunctions()[1].(x))\n", + "y3 = map(v -> v[3], oneDTestFunctions()[1].(x))\n", + "plot(x, y1, ylims=(-3,3), label=L\"$f(x)$\")\n", + "plot!(x, y2, label=L\"$\\frac{d}{dx}f(x)$\")\n", + "plot!(x, y3, label=L\"$\\frac{d^2}{dx^2}f(x)$\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3926fcb9-62b8-45d3-ae64-8c626ac04901", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Golden ratio search\n", + "feval\trel gap\t\tx_-\t\tx_+\n", + " 2\t4.3255e-01\t-5.00000000e-01\t3.00000000e+00\n", + " 3\t4.3255e-01\t-5.00000000e-01\t1.66311896e+00\n", + " 4\t2.0218e-01\t-5.00000000e-01\t8.36881039e-01\n", + " 5\t3.3976e-04\t-5.00000000e-01\t3.26237921e-01\n", + " 6\t3.3976e-04\t-1.84405197e-01\t3.26237921e-01\n", + " 7\t3.3976e-04\t-1.84405197e-01\t1.31189606e-01\n", + " 8\t3.3976e-04\t-6.38587088e-02\t1.31189606e-01\n", + " 9\t3.3976e-04\t-6.38587088e-02\t5.66877794e-02\n", + " 10\t3.3976e-04\t-1.78140475e-02\t5.66877794e-02\n", + " 11\t3.3976e-04\t-1.78140475e-02\t2.82306137e-02\n", + " 12\t1.5573e-07\t-1.78140475e-02\t1.06431181e-02\n", + " 13\t1.5573e-07\t-6.94437747e-03\t1.06431181e-02\n", + " 14\t1.5573e-07\t-6.94437747e-03\t3.92529259e-03\n", + " 15\t1.5573e-07\t-2.79253295e-03\t3.92529259e-03\n", + " 16\t1.5573e-07\t-2.79253295e-03\t1.35931156e-03\n", + " 17\t1.5573e-07\t-1.20666946e-03\t1.35931156e-03\n", + " 18\t1.5573e-07\t-1.20666946e-03\t3.79194024e-04\n", + " 19\t1.5573e-07\t-6.00923513e-04\t3.79194024e-04\n", + " 20\t7.0543e-11\t-2.26551927e-04\t3.79194024e-04\n", + " 21\t7.0543e-11\t-2.26551927e-04\t1.47819659e-04\n", + " 22\t7.0543e-11\t-8.35547054e-05\t1.47819659e-04\n", + " 23\t7.0543e-11\t-8.35547054e-05\t5.94425162e-05\n", + " 24\t7.0543e-11\t-2.89346270e-05\t5.94425162e-05\n", + " 25\t7.0543e-11\t-2.89346270e-05\t2.56854513e-05\n", + " 26\t7.0543e-11\t-8.07161358e-06\t2.56854513e-05\n", + " 27\t7.0543e-11\t-8.07161358e-06\t1.27913999e-05\n", + " 28\t3.1963e-14\t-8.07161358e-06\t4.82243784e-06\n", + " 29\t3.1963e-14\t-3.14652419e-06\t4.82243784e-06\n", + " 30\t3.1963e-14\t-3.14652419e-06\t1.77856520e-06\n", + " 31\t3.1963e-14\t-1.26530744e-06\t1.77856520e-06\n", + " 32\t3.1963e-14\t-1.26530744e-06\t6.15909307e-07\n", + " 33\t3.1963e-14\t-5.46746585e-07\t6.15909307e-07\n" + ] + }, + { + "data": { + "image/png": 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eOCu0g1Cr1X7++efr169v8nxxcfG6deuolAThQSlOMlirGMLQLiR8mB11uDc9cFNoB+HmzZsPHjy4aNGixk96PJ6FCxcuXLiQVlUQBqKEMVHCGKvDQLuQ8IHltoGzQjsIFy9e/Le//U0oFDZ+cs2aNTNmzIiKwn88wS8qSTLuQRFAetybHrgqtIPwVoWFhYcPH/7Vr35FuxAIeUpJkh4TRwMHM2WAs4St78IBjz/++MmTJxs/88477wwcOLDJbl6vd+HChW+//XYQS4OwpZIk6S0IwoAx4NQocFXbgvDSpUuXLl1KTU3t16+fSCRq8irDMEeOHKmurh4yZEhCQoKPbdrt9sLCwsTExJSUlMZNHT16tKqqavDgwWq1+v333/elKY1Gc+7cuenTpxNCrFZrfn7+8ePHeTyej5UANKbCXQkDSm/VdkjoTrsKgGb4emrU6/U+9thjo0aN+uc///nMM89s3ry5yQ4Mwzz44IOPPfbYBx980LVr1yNHjvjS7KOPPqpUKgcOHLhhw4bGzz/00ENz58794IMPunXrdvjw4du9vbKy8sSJEx6P58SJExUVFWlpaeXl5QUFBQUFBRKJpKCgACkId0wlSdLbEIQBo7dq46Qpre8HEHS+BuG6deuOHj168eLFzz777Mcff5w3b16THQ4cOHDs2LGjR49+8sknL7zwwh//+Ef2ebvd/sknnzTec8eOHU6nk91+/vnna2pqJk6c2HiHgwcP/vDDD2xTL7744pIlS25X1fHjx9etWzdz5sx169YdO3as8UuzZ8/28aMBNEslScbiMgGkt1ZhoVHgJl9PjW7ZsmXRokV2u72kpKRbt27R0U1vrblz586JEydKpVJCyIwZM5577jmj0ahQKIxG44svvlheXs5e5LBq1aqtW7eOHDmSbaFbt263Hmvnzp0TJkyQyWRsU4sWLaqrq4uLi7t1z0mTJk2aNKnZgt94440WPs6ZM2d27ty5cuVK9mFMTMzJkyfT09Nb/Tk05nQ6+Xz+raeIITxEEZnOVGk2m33c32KxtGs9Ic3jdVudRp47uuWfp81m83g8OJEDhBCGYWw2G8P4dS2v1+v15evkaxAWFRXt3LnzzTfflMlkZWVlX375ZY8ePRrvUFFRkZ+fz26npKQIhcKKigqFQpGcnHzw4MERI0Y4HA6hULh58+YDBw60PIJYUVHRq1cvdjspKSk6OrqioqLZILxjvXv37t69+4oVK/xpJCoqCkEYxsSSWKvLIJGI+XyBj29h//cGt6qzVMpj4xVyRcu78fl8sViMIARCCMMwfD6f7VzdMa/Xa7fbW93N1yC0Wq1CofDUqVM8Hu/555///e9/v2fPnsY7uFyuhkjg8XhCobDh/GdSUtLevXv79OnDtpCU1MrpkcZNEUJEIlFDUwBBI+ALpTEqo12HSf/+w7UTwGW+jhGmpKSMGTOG/Z/auHHjTp06desONTU17LbZbLbb7Y1ngX7wwQeJiYlyufxf//qXL8dqaMpms1kslsZNAQSNSox7UARGnUWjkuC3GDjK1yAcNmzYjRs32O3S0tKGXp3b7WY3hgwZcuDAAXb722+/zcvLa9jn9ddf37Jly969ew8ePPj++++vWrWq5WM1aSo3NxdBCFSopMl1CMJA0Fu1cbgBE3CVr6dGf//7348YMSIhIUEmky1btowNM5fLFR0dXVBQ0K9fv4ceemj58uVPPPFE//79ly9f/uc//5ntPpaUlGzfvv3gwYOJiYmEkL17944fP37WrFnszJTPP//8xx9/PHfuXFVVlcVimTJlyoABA6ZNm7Zs2bLHH3980KBBK1asWLJkCZ8fbivgQEhQYXGZANFbq1JVebSrAGierwHTo0ePAwcOaLXaq1evbt++/ZFHHiGECIXCV199lY202NjYw4cPp6SknDp16u233264viI7O/v48eNsChJCUlNTT5w40TA/UywWq1SqOXPmjB8/XqVSsVNJY2Jifvjhh7S0tJMnT77xxhvz588P7GcG8BGuqQ8UvVWrkuDe9MBRPD8np4aoZcuWud1uP2eN4vKJsHf2xn+PFn3x+MjXfdnZbDZj1ujtvPz59EeHvZTWWqfQarVi1iiwGIaxWq0BmTUqkUha3g2nHAFuSyVJrrNoaFcRDvQWbRwWGgWuQhAC3JZKklxnRRD6y+6yMISJjUJ3GTgKQQhwW9IYpdtT76y30S4ktBmsVXG4dgI4DEEI0BJMHPVfnVWDGzABlyEIAVqikuBSQn/VWXDfCeA0BCFAS+KkyegR+kmPHiFwG4IQoCUqSQruU+8nvVWLhUaByxCEAC1Bj9B/uCUvcByCEKAlKkkKxgj9VGfRIAiByxCEAC3BNfV+8njdZodeEaumXQjAbSEIAVqikiSa7Dov46VdSKgy2qplMXG+39wYIPgQhAAtEfBF0mil0VZDu5BQVYcbMAHnIQgBWqGSYL7MnauzaOKkqbSrAGgJghCgFXHSFAwT3jG9RYP11YDjEIQArUAQ+qPOiimjwHUIQoBWxElT6iyVtKsIVXUWLCsDXIcgBGiFSpKCMcI7VmfRxmOMELgNQQjQinicGr1TDGEMtiol1lcDbkMQArQCN6C4Yxa7PloojhLG0C4EoCUIQoBWRIvEUcJoi0NPu5DQg5kyEBIQhACtw0Jrd6bWUqlCEALnIQgBWhcnTcXE0Tugt2jiMGUUOA9BCNC6OCnuQXEncN8JCAkIQoDWxUtTa9EjbDuMEUJIQBACtA5jhHem1lKJhUaB+xCEAK2LxxjhHanDQqMQChCEAK2Lw6nRtrM4DAK+MDZKSrsQgFYgCAFaFxsl5fMENqeJdiGhRG9FdxBCA4IQwCeYL9NWteaKeFka7SoAWocgBPAJ7kHRVrUWDZbbhpCAIATwCXqEbYVrJyBUIAgBfBInTcUVFG1SZ6lEEEJIQBAC+CROmoIeYZvgIkIIFQhCAJ/gUsK2qsMYIYQIBCGAT+JlaegR+s7i0Av5ohiRhHYhAK1DEAL4JEYkEfCFFoeBdiGhAedFIYQgCAF8hbOjvqu1VOIiQggVCEIAX8VLcXbUV7XmCgwQQqhAEAL4Kl6WWmupoF1FaMBMGQghCEIAX8VL02rN6BH6pA5jhBA6EIQAvorDGKHPdOaKBIwRQohAEAL4Kl6WqsOpUR8whNFbtSpJMu1CAHyCIATwVbw0VW/RMoyXdiFcZ7TVxEbJooQxtAsB8AmCEMBXIkF0bJTMaNfRLoTras24dgJCCYIQoA3iZZgv07paSzkGCCGEIAgB2iBemlprKaddBdfVmitx7QSEEAQhQBskoEfog1pLRbwUPUIIGQhCgDaIl6bpzJg42gqMEUJoQRACtEG8LA2Ly7RKZ8FFhBBKEIQAbZCAmzG1xu1xWR16hTiRdiEAvkIQArSBUpxodejrPS7ahXBXraVSKUnm8/C3BUIGvqwAbcDj8VWSFCy01oJaC+47ASEGQQjQNvGyVMyXaYHOVK6Wp9OuAqANEIQAbZMgS6s141LC26q1VOLaCQgtCEKAtomXpmPp7RbozOW4dgJCC4IQoG0S5Om1ODV6e7XmigQZTo1CKEEQArRNgjRNh1Ojt6ezIAghxCAIAdomQZauM5czhKFdCBeZ7XUiQVSMSEK7EIA2QBACtE20SBwtFJvtdbQL4SKdpRzdQQg5CEKANouX4exo83Sm8ngEIYQaBCFAmyUgCG9DZ0aPEEIPghCgzRJkmDjaPJ25Qo0ghFCDIARoswRZBnqEzaq14CJCCD0IQoA2S5Cl1SAIm1NjKlPLMmhXAdA2IR+EDMPo9fomT7rd7lufBAiUBFm6zlxGuwrOcbkdjnqrXJxAuxCAtgntIFy6dGleXl5qatOl7hcvXqxWq6mUBJFALk5w1tsd9VbahXCLzlweL03jER7tQgDaJrSD8LnnnisqKoqOjm785NGjR51Op0SCS3qhvfAIDxNHb4VVRiFEhXYQyuXyJs84nc4XX3xxxYoVVOqByMGuL0O7Cm6pMd1IlGfSrgKgzYS0C/DJRx99VFb2syGZadOmdejQ4dY9//KXvzz11FNKpTJYpUGEUsszakwYJvyZGnN5miqPdhUAbRYaQZiQkOD1ehs/ExMTc+tuOp1u27ZtSqVy9erVTqdz9erVzz//fLBqhMiSIEsvq71Muwpu0ZnLe2eOoF0FQJu1LQgdDsfkyZNVKtX27dtvfXXHjh1Lliypq6sbPXr0unXrFApFqw1u3bp1165dRUVFCxYsmDdvXsPzn3zyyQsvvFBbW3vfffetW7du1KhRvpQnFotff/11dpvP5+fk5Pj2sQDaTC3LOFmyl3YV3FJjKlPLce0EhJ62jREuX768trb20qVLt75UWlo6b968DRs2lJWV1dfXv/DCCw0vGY3Gxns2fqjT6e69997Y2FiNRtPwZHl5+Zw5c9atW1dWVub1elvo1W3evHn06NEikWj06NEbNmwQi8XT/ketVk+bNq1Nnw7AdwlyjBH+jNvjMtl1Kkky7UIA2qwNPcLTp0/v3bv3t7/97Zo1a259dcuWLaNHj7733nsJIX/605/uvffev//971FRUSUlJcOGDduzZ0+3bt0IIWfOnBk/fvyRI0fS09MJIYsWLSKE7N+/v0lTI0eOHDFiBCFk6dKlgwcPfuONN5o9Fzp79uzZs2c3W21paWkLn6W0tPTUqVMN6cvj8f785z8nJLTt+ien08nn8z0eT5veBeFBLFBZHHqz1SgS3Jy07HA4RCIR3aooqjaVKsVJ9S53PXHfcSMOh4PP5/N4uAADCMMwDodDKPRr/M7r9TYZVmuWr8dwu93z589/7733Ll9uflzkypUrvXr1Yrd79uxpsVgqKiqys7Ozs7P//ve/jx49es+ePR6PZ9y4ce+88w6bgrdz5cqV3r17s9s9evSw2+3l5eUdO3b0sVRfyOXy1NTU/Px89mF0dLRSqWzrXzGv18vn8yP5b1+Ei5OmmJzVyYqbZ+BFIlEkfxmMjiq1LN3PnwD7M0QQAiGEYRj/f6cCHIQrV64cNWpU3759bxeEdXV1MpnsZqNCoVgsrq2tzc7OJoRMnTrV4/GMHTvW6/W+++67kyZNavlYdXV1PXr0YLf5fL5UKq2trQ1sEKpUqr59+y5YsMCfRgQCAZ/PFwgEgaoKQotanlFrqUiLuzlPUiAQRPKXQWcpT1R08PMnwP4MEYRACGEYxv/fKR6P58vXyacxwmvXrr333nvjx48/ceJESUmJzWY7ceJEk5iNi4szm83sdn19vc1ma3ymsXPnzm63mxCSl9f67OrGTXk8HovF0taTlgBBoMbS243oMFMGQpZPPUK9Xp+WlsaO59XV1VVVVT3xxBOHDh2KjY1t2KdTp05nz55lty9cuCCRSFJSUtiHZ86ceeCBB9577z23282eI23o8DWrU6dOBQUF7PbFixdjY2PT0rBcBXBOgjxDo79GuwquqDbd6J4xlHYVAHfCpx5hfn5+wf+sXLmSDarY2FiPx/Pwww+XlJQQQh599NFvvvnm+++/t9lsK1eunDVrFrvyWWlp6QMPPMCeEZ06deobb7wxduzYioqb93LT6XTFxcVWq1Wv1xcXF7MdwV//+tf79+//7rvv2KZmzpzZ7EwZALoS5Zk1phu0q+AKnRn3nYBQ1eYl1uLi4rp27cpuMwxz4cIFh8NBCMnKylq/fv2jjz6amprK5/NXrVrF7pORkfHFF1/84he/YB8++OCDn3/+ecMy2Rs2bJg+fXp1dfXBgwenT5/OTh/NzMzcuHHj3LlzU1NTPR7Pq6++6v/nBAi4BFlGNYKQEEKIx1tvtNXESZsufw8QEngMw9CugYJly5a53W4/lyRlL5+I5ImCEc7LeJ/bOmT1zP+yV1CYzeaG+WKRptpYunb/wqVTPvOzHavVKhaLMVkGCCEMw1itVqlU6k8jXq/Xbre3eg+G0F50G4AiPo8fJ03FfBlCSLXpBs6LQuhCEALcOSy9zao2lapx3wkIWQhCgDuXKO+A+YGfSTwAAB6xSURBVDKEkBpTGW7ABKELQQhw59RyzJchhJAa0w30CCF0IQgB7pxanokgJOwYIYIQQhaCEODO4VJCQki9x2lx1MVJU2gXAnCHEIQAd04pSbK7zM56G+1CaKoxlcVL0/g8/DGBUIXvLsCd4xGeOuIvq8cAIYQ6BCGAX9SKSJ84WmUsTVR0oF0FwJ1DEAL4JTHi58vUmG8kyhGEEMIQhAB+SZR3qDaV0q6CpmpjaaICp0YhhCEIAfySqMisNkZ2EJpK0SOEkIYgBPBLkjwrknuENqfJ43XLY+NpFwJw5xCEAH4RR8v5PIHZXke7EDqqTNfRHYRQhyAE8FeSIqvKdJ12FXTg2gkIAwhCAH8lKjpE7DBhlfF6kiKLdhUAfkEQAvgrkieO4iJCCAMIQgB/JSmyqozXaVdBR7WxNAljhBDiEIQA/kpUdKg2RuI19V7GqzOXY4wQQh2CEMBfalm63qrxeOtpFxJsdZZKWWx8lDCGdiEAfkEQAvhLwBcpJcm11krahQRblbE0CQOEEPoQhAABkKzI1pnLaFcRbNWYMgphAUEIEABJiqwac8QNE+LaCQgPCEKAAEhSdIjAINQaS5IU2bSrAPAXghAgAJKU2TXmiLuUsNpYih4hhAEEIUAAJCuyI+2aepvT5PbWY7ltCAMIQoAAiI2SiYTRJruOdiHBozWWJOO8KIQFBCFAYCTKOmgNJbSrCJ4qY0mSMot2FQABgCAECAy1rIPWGEFBqDWgRwhhAkEIEBiJ8g5VkdYjRBBCWEAQAgSGWpoZYT1CXEQIYQJBCBAYankEjRG63A6zozZelka7EIAAQBACBIYiVl3vcdhcJtqFBEOV8bpansnn4Q8IhAN8jwECJkmRFSHDhFW4dgLCCIIQIGCSlTmayAhCjaE4WZlDuwqAwEAQAgRMsiK7KjLmy2gNxclK9AghTCAIAQImWZmrMVyjXUUwaI0l6BFC2EAQAgRMijJbayimXUW7c3tceotWLcugXQhAYCAIAQJGJU2xucx2l4V2Ie2ryng9QZ4u4AtpFwIQGAhCgIDhEV4kDBNqDcUpCpwXhfCBIAQIpGRlTtgPE1YarqWoOtKuAiBgEIQAgZSizNWE+zCh1lCcgpkyEEYQhACBlKLK1eiLaFfRvnARIYQZBCFAIKUoc8K7R1jvcRpt1ZgyCuEEQQgQSEpJksvtsDnDdsVRraE4Ud6BzxfQLgQgYBCEAIHEI7zwni+jMVzDeVEIMwhCgABLVeZW6sM2CCv1RamYMgrhBUEIEGApqlyNIWzny2j011KUubSrAAgkBCFAgKWq8irDd+KoxnAtVYUghLCCIAQIsBRlbqW+iCEM7UICz+Yy2VxmlTSFdiEAgYQgBAgwaYxSJIgyWqtpFxJ4lfqiVFUej/BoFwIQSAhCgMBLVeVVhOPZUcyUgbCEIAQIvFRVx0r9VdpVBF5l3dU0VR7tKgACDEEIEHjhOl8GPUIISwhCgMALyx4hQxiN4VoKpoxC2EEQAgReijJHZy73eOtpFxJIteYKcbRcHCWnXQhAgCEIAQJPKIiKl6ZpDWF1h96KusJUDBBCOEIQArSL1Li8Cn0h7SoCqVJfhJkyEJYQhADtIk2VV1EXVsOE5XVX0uI60a4CIPAQhADtIi2uU0V4zZdBjxDCFYIQoF2kx3Uqr71Mu4qAcdRbzY46tRz344UwhCAEaBfy2AQ+X2CwhclCaxV1hSnKXB4PfzEgDOFrDdBe0lSdKurCZL5MhR5rykDYQhACtJf0uM7ldVdoVxEY5bVX0uM7064CoF0gCAHaS3pcp/Jw6RGW111Oj+tCuwqAdhHaQfjNN9889dRTM2fObPL8V199NX36dColATRIi+9cERY9Qo/XXWW8jvvxQrgK7SB0u90TJ0788ssvGz9ZV1f31ltv7d69m1ZVAKwkeQeTvdbustAuxF9aQ3GcJCVKGEu7EIB2EdpB+MADD4wdO5bP/9mneO6555YvXy4QCGhVBcDi8fipyo5hsL5MeV0hBgghjAlpF+ATvV7vcrkaP6NSqaKiom7dc/fu3Wq1ul+/fsEqDaAlGfFdymovd0zqS7sQv2CAEMJbaAThqlWrzp071/iZl156qU+fPk12s9vtS5cu3bx5c3FxsdfrLS4uzsnJCWKZAE2lx3cu0p6iXYW/ymov9cy4l3YVAO3F1yA8f/787t27b9y4kZ6ePmfOnKSkpFv3qaqqWrt2bU1NzdixYydMmOBLs1VVVQUFBZcuXRo8ePDgwYMbnq+url67dm11dfWYMWMmTpy4evVqX1qz2Ww5OTnLly8nhDidzsWLF3/00Uc8Hs+3jwgQeBnxXQ9c3E67Cr8wjLei7ipOjUIY8zUIH3744VGjRnXv3v3777/v1avX6dOnU1JSGu9gt9sHDx48YsSIAQMGPPnkk9XV1XPnzm212blz5xoMhvLycofD0RCETqdzyJAhw4YNGzhw4FNPPaXVah9//PFm337o0KHDhw87nc7Vq1cPGjTo3nvv3bFjB/uSQqFo2AagJUWZU2uucLntoTvTpNp0QxqjxG0IIYz5GoQnT54UiUSEkCeffLJv377/+c9/nnjiicY77NixQ6VSrV+/nhCSmJj4/PPPz5kzh8fjlZWVrVy58s0332SH9JxO51NPPbVixQo2R3ft2kUImTZtWuOmPv74Y6lUun79eh6Pl5KS8swzzzz22GPNduxUKlVOTs4///lPQkhcXFzjl7Zs2dLCx7Hb7eXl5fv27WMfRkdHDx48GPNrIOAEfFGSIrtCfzVb3Yt2LXeorPZyenxX2lUAtCNfg5BNQUIIwzAWi0WpVDbZ4fvvvx85ciS7PWrUqMLCwurq6qSkpNTUVIvFMnny5E8//ZTH402bNk0sFqvV6haOxTbFJt+oUaOuXbum0WhSU1Nv3bNHjx49evRotpFf/vKXLRyirKzshx9+qKysbHjm3XffbfYQLXA6nXw+v+EnAxHOYmn+MokUeceiitMJMdlBridQrmnOJElzzGZzEI5ls9k8Hg+GM4AQwjCMzWZjGMafRrxery9fpzZPllmzZo1IJLo1ZjQaTV7ezaUIZTJZTExMZWVlUlKSQCDYsmXLzJkzH3roIZfLFR8fv3nz5pb7XhqNZtCgQey2WCyWSCSVlZVtTamWderUKScnZ8WKFf40EhUVhSCExmQy2a1P5qT0Kqk+2+xLIUFrvjY257Hg1M/n88ViMYIQCCEMw/D5fKlU6k8jXq/Xbre3ulvbgvCjjz567bXXDhw4EB0d3eSlqKgot9vNbjMM4/F4GvYRCoUbN27Mzs6OiooqKipq9Qxk46YIIW63+9bDAYSKzPhu3136iHYVd4hhvBV1VzLice0EhLM2XFC/c+fORYsWff311507NzN/LC0trby8nN3WarVut7uhD+d0OqdPnz5u3LihQ4c+/PDDTa4IbLmp6upqp9OZlpbme50AnJKq6lhrqXS5W/9vKQdVmUqlMSrMlIHw5msQfv31108++eQXX3zRq9dPY/4Mw+zevdtkMhFCJk2a9OWXX1qtVkLIhx9+OHz4cHYc0eVyTZs2TaFQbNy4cfv27VKpdPLkyU6ns4VjTZo0adeuXeyIy4cffjhs2LAmE2EAQoiAL0xR5oTo6tvltZczMFMGwp2vQThjxgxCyIIFC/Lz8/Pz899++21CSH19/fjx469evUoIGTVqVL9+/QYOHDh9+vSXX3555cqV7Bt1Ol3Xrl23bt0qFAoFAsHmzZu7detmNBrZV1evXp2fn79///533303Pz//s88+I4QMHz580KBBbFMrVqx46aWXAv6xAYIpM75bqe4C7SruRKnuQmZ8N9pVALQvX8cIDxw44PV6Gx4mJycTQkQi0ZkzZ9g5Mjwe79NPPz1y5Eh1dfU777yTkJDA7pmamtr4cnihUPjXv/614eHDDz983333NTzs0KED29THH3/MNvX222+3PMUUgPsyE7pfrjxCu4o7UVpzoXf+SNp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15\t4.5206e-08\t-1.22070312e-04\t7.32421875e-04\t3.05175781e-04\t1.8505e-03\n", + " 16\t2.5423e-08\t-1.22070312e-04\t3.05175781e-04\t9.15527344e-05\t5.5534e-04\n", + " 17\t7.0626e-10\t-1.22070312e-04\t9.15527344e-05\t-1.52587891e-05\t-9.2572e-05\n", + " 18\t7.0626e-10\t-1.52587891e-05\t9.15527344e-05\t3.81469727e-05\t2.3141e-04\n", + " 19\t3.9726e-10\t-1.52587891e-05\t3.81469727e-05\t1.14440918e-05\t6.9426e-05\n", + " 20\t1.1035e-11\t-1.52587891e-05\t1.14440918e-05\t-1.90734863e-06\t-1.1571e-05\n", + " 21\t1.1035e-11\t-1.90734863e-06\t1.14440918e-05\t4.76837158e-06\t2.8928e-05\n", + " 22\t6.2073e-12\t-1.90734863e-06\t4.76837158e-06\t1.43051147e-06\t8.6784e-06\n", + " 23\t1.7243e-13\t-1.90734863e-06\t1.43051147e-06\t-2.38418579e-07\t-1.4464e-06\n", + " 24\t1.7243e-13\t-2.38418579e-07\t1.43051147e-06\t5.96046448e-07\t3.6160e-06\n", + " 25\t9.6989e-14\t-2.38418579e-07\t5.96046448e-07\t1.78813934e-07\t1.0848e-06\n", + " 26\t2.6941e-15\t-2.38418579e-07\t1.78813934e-07\t-2.98023224e-08\t-1.8080e-07\n" + ] + }, + { + "data": { + "image/png": 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6\t1.0638e-02\t-5.00000000e-01\t3.01841963e-01\t6.12893739e-02\t3.3443e-01\n", + " 7\t1.0638e-02\t-5.00000000e-01\t6.12893739e-02\t-1.07097438e-01\t-7.4538e-01\n", + " 8\t2.5086e-04\t-1.07097438e-01\t6.12893739e-02\t9.13789778e-03\t5.4637e-02\n", + " 9\t2.5086e-04\t-1.07097438e-01\t9.13789778e-03\t-2.57327030e-02\t-1.6225e-01\n", + " 10\t5.3189e-06\t-2.57327030e-02\t9.13789778e-03\t-1.32328247e-03\t-8.0445e-03\n", + " 11\t5.3189e-06\t-1.32328247e-03\t9.13789778e-03\t1.81507161e-03\t1.0980e-02\n", + " 12\t4.3014e-11\t-1.32328247e-03\t1.81507161e-03\t3.76571780e-06\t2.2845e-05\n", + " 13\t4.3014e-11\t-1.32328247e-03\t3.76571780e-06\t-3.94348738e-04\t-2.3939e-03\n", + " 14\t4.3014e-11\t-3.94348738e-04\t3.76571780e-06\t-1.15668619e-04\t-7.0185e-04\n", + " 15\t4.3014e-11\t-1.15668619e-04\t3.76571780e-06\t-3.20645832e-05\t-1.9453e-04\n", + " 16\t4.3014e-11\t-3.20645832e-05\t3.76571780e-06\t-6.98337251e-06\t-4.2366e-05\n", + " 17\t5.1438e-21\t-6.98337251e-06\t3.76571780e-06\t4.11796824e-11\t2.4982e-10\n" + ] + }, + { + "data": { + "image/png": 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2\t2.2210e-03\t-2.66964885e-02\t2.2210e-03\t-1.6855e-01\t6.5539e+00\n", + " 3\t2.9054e-06\t-9.78183164e-04\t2.9054e-06\t-5.9434e-03\t6.0852e+00\n", + " 4\t6.7444e-12\t-1.49111486e-06\t6.7444e-12\t-9.0461e-06\t6.0667e+00\n", + " 5\t3.6771e-23\t-3.48170895e-12\t3.6771e-23\t-2.1122e-11\t6.0667e+00\n" + ] + }, + { + "data": { + "image/png": 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"text/plain": [ + "(-3.481708945640477e-12, \"optimal\")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "include(\"UNM.jl\")\n", + "UNM(oneDTestFunctions()[1]; x=0.1, Plotg=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "044fd698-e21d-4b00-a141-4a9ea7a6f486", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Golden ratio search\n", + "feval\trel gap\t\tx_-\t\tx_+\n", + " 2\t4.3255e-01\t-5.00000000e-01\t3.00000000e+00\n", + " 3\t4.3255e-01\t-5.00000000e-01\t1.66311896e+00\n", + " 4\t2.0218e-01\t-5.00000000e-01\t8.36881039e-01\n", + " 5\t3.3976e-04\t-5.00000000e-01\t3.26237921e-01\n", + " 6\t3.3976e-04\t-1.84405197e-01\t3.26237921e-01\n", + " 7\t3.3976e-04\t-1.84405197e-01\t1.31189606e-01\n", + " 8\t3.3976e-04\t-6.38587088e-02\t1.31189606e-01\n", + " 9\t3.3976e-04\t-6.38587088e-02\t5.66877794e-02\n", + " 10\t3.3976e-04\t-1.78140475e-02\t5.66877794e-02\n", + " 11\t3.3976e-04\t-1.78140475e-02\t2.82306137e-02\n", + " 12\t1.5573e-07\t-1.78140475e-02\t1.06431181e-02\n", + " 13\t1.5573e-07\t-6.94437747e-03\t1.06431181e-02\n", + " 14\t1.5573e-07\t-6.94437747e-03\t3.92529259e-03\n", + " 15\t1.5573e-07\t-2.79253295e-03\t3.92529259e-03\n", + " 16\t1.5573e-07\t-2.79253295e-03\t1.35931156e-03\n", + " 17\t1.5573e-07\t-1.20666946e-03\t1.35931156e-03\n", + " 18\t1.5573e-07\t-1.20666946e-03\t3.79194024e-04\n", + " 19\t1.5573e-07\t-6.00923513e-04\t3.79194024e-04\n", + " 20\t7.0543e-11\t-2.26551927e-04\t3.79194024e-04\n", + " 21\t7.0543e-11\t-2.26551927e-04\t1.47819659e-04\n", + " 22\t7.0543e-11\t-8.35547054e-05\t1.47819659e-04\n", + " 23\t7.0543e-11\t-8.35547054e-05\t5.94425162e-05\n", + " 24\t7.0543e-11\t-2.89346270e-05\t5.94425162e-05\n", + " 25\t7.0543e-11\t-2.89346270e-05\t2.56854513e-05\n", + " 26\t7.0543e-11\t-8.07161358e-06\t2.56854513e-05\n", + " 27\t7.0543e-11\t-8.07161358e-06\t1.27913999e-05\n", + " 28\t3.1963e-14\t-8.07161358e-06\t4.82243784e-06\n", + " 29\t3.1963e-14\t-3.14652419e-06\t4.82243784e-06\n", + " 30\t3.1963e-14\t-3.14652419e-06\t1.77856520e-06\n", + " 31\t3.1963e-14\t-1.26530744e-06\t1.77856520e-06\n", + " 32\t3.1963e-14\t-1.26530744e-06\t6.15909307e-07\n", + " 33\t3.1963e-14\t-5.46746585e-07\t6.15909307e-07\n", + "Dichotomic search\n", + "feval\trel gap\t\tx_-\t\tx_+\t\tx\t\tf'(x)\n", + " 3\t5.1784e-01\t-5.00000000e-01\t3.00000000e+00\t1.25000000e+00\t5.8857e-01\n", + " 4\t2.4399e-01\t-5.00000000e-01\t1.25000000e+00\t3.75000000e-01\t8.3549e-01\n", + " 5\t1.2585e-02\t-5.00000000e-01\t3.75000000e-01\t-6.25000000e-02\t-4.1386e-01\n", + " 6\t1.2585e-02\t-6.25000000e-02\t3.75000000e-01\t1.56250000e-01\t6.9460e-01\n", + " 7\t6.3294e-03\t-6.25000000e-02\t1.56250000e-01\t4.68750000e-02\t2.6271e-01\n", + " 8\t1.8664e-04\t-6.25000000e-02\t4.68750000e-02\t-7.81250000e-03\t-4.7971e-02\n", + " 9\t1.8664e-04\t-7.81250000e-03\t4.68750000e-02\t1.95312500e-02\t1.1480e-01\n", + " 10\t1.0350e-04\t-7.81250000e-03\t1.95312500e-02\t5.85937500e-03\t3.5219e-02\n", + " 11\t2.8958e-06\t-7.81250000e-03\t5.85937500e-03\t-9.76562500e-04\t-5.9335e-03\n", + " 12\t2.8958e-06\t-9.76562500e-04\t5.85937500e-03\t2.44140625e-03\t1.4754e-02\n", + " 13\t1.6260e-06\t-9.76562500e-04\t2.44140625e-03\t7.32421875e-04\t4.4383e-03\n", + " 14\t4.5206e-08\t-9.76562500e-04\t7.32421875e-04\t-1.22070312e-04\t-7.4070e-04\n", + " 15\t4.5206e-08\t-1.22070312e-04\t7.32421875e-04\t3.05175781e-04\t1.8505e-03\n", + " 16\t2.5423e-08\t-1.22070312e-04\t3.05175781e-04\t9.15527344e-05\t5.5534e-04\n", + " 17\t7.0626e-10\t-1.22070312e-04\t9.15527344e-05\t-1.52587891e-05\t-9.2572e-05\n", + " 18\t7.0626e-10\t-1.52587891e-05\t9.15527344e-05\t3.81469727e-05\t2.3141e-04\n", + " 19\t3.9726e-10\t-1.52587891e-05\t3.81469727e-05\t1.14440918e-05\t6.9426e-05\n", + " 20\t1.1035e-11\t-1.52587891e-05\t1.14440918e-05\t-1.90734863e-06\t-1.1571e-05\n", + " 21\t1.1035e-11\t-1.90734863e-06\t1.14440918e-05\t4.76837158e-06\t2.8928e-05\n", + " 22\t6.2073e-12\t-1.90734863e-06\t4.76837158e-06\t1.43051147e-06\t8.6784e-06\n", + " 23\t1.7243e-13\t-1.90734863e-06\t1.43051147e-06\t-2.38418579e-07\t-1.4464e-06\n", + " 24\t1.7243e-13\t-2.38418579e-07\t1.43051147e-06\t5.96046448e-07\t3.6160e-06\n", + " 25\t9.6989e-14\t-2.38418579e-07\t5.96046448e-07\t1.78813934e-07\t1.0848e-06\n", + " 26\t2.6941e-15\t-2.38418579e-07\t1.78813934e-07\t-2.98023224e-08\t-1.8080e-07\n", + "Dichotomic search with safeguarded interpolation (0.5000)\n", + "feval\trel gap\t\tx_-\t\tx_+\t\tx\t\tf'(x)\n", + " 3\t5.1782e-01\t-5.00000000e-01\t3.00000000e+00\t1.24996500e+00\t5.8848e-01\n", + " 4\t2.4399e-01\t-5.00000000e-01\t1.24996500e+00\t3.75000000e-01\t8.3549e-01\n", + " 5\t1.2582e-02\t-5.00000000e-01\t3.75000000e-01\t-6.24912502e-02\t-4.1379e-01\n", + " 6\t1.2582e-02\t-6.24912502e-02\t3.75000000e-01\t1.56250000e-01\t6.9460e-01\n", + " 7\t6.3299e-03\t-6.24912502e-02\t1.56250000e-01\t4.68771874e-02\t2.6272e-01\n", + " 8\t1.8633e-04\t-6.24912502e-02\t4.68771874e-02\t-7.80593770e-03\t-4.7931e-02\n", + " 9\t1.8633e-04\t-7.80593770e-03\t4.68771874e-02\t1.95350780e-02\t1.1483e-01\n", + " 10\t1.0368e-04\t-7.80593770e-03\t1.95350780e-02\t5.86429675e-03\t3.5248e-02\n", + " 11\t2.8610e-06\t-7.80593770e-03\t5.86429675e-03\t-9.70683769e-04\t-5.8978e-03\n", + " 12\t2.8610e-06\t-9.70683769e-04\t5.86429675e-03\t2.44673814e-03\t1.4787e-02\n", + " 13\t1.6508e-06\t-9.70683769e-04\t2.44673814e-03\t7.37993013e-04\t4.4720e-03\n", + " 14\t4.1053e-08\t-9.70683769e-04\t7.37993013e-04\t-1.16328291e-04\t-7.0585e-04\n", + " 15\t4.1053e-08\t-1.16328291e-04\t7.37993013e-04\t3.10823818e-04\t1.8847e-03\n", + " 16\t2.8681e-08\t-1.16328291e-04\t3.10823818e-04\t9.72434918e-05\t5.8985e-04\n", + " 17\t2.7609e-10\t-1.16328291e-04\t9.72434918e-05\t-9.54026395e-06\t-5.7878e-05\n", + " 18\t2.7609e-10\t-9.54026395e-06\t9.72434918e-05\t4.38505461e-05\t2.6601e-04\n", + " 19\t2.7609e-10\t-9.54026395e-06\t4.38505461e-05\t1.71546072e-05\t1.0407e-04\n", + " 20\t4.3960e-11\t-9.54026395e-06\t1.71546072e-05\t3.80690466e-06\t2.3095e-05\n", + " 21\t2.4925e-11\t-9.54026395e-06\t3.80690466e-06\t-2.86654617e-06\t-1.7390e-05\n", + " 22\t6.7038e-13\t-2.86654617e-06\t3.80690466e-06\t4.70112506e-07\t2.8520e-06\n", + " 23\t6.7038e-13\t-2.86654617e-06\t4.70112506e-07\t-1.19818347e-06\t-7.2690e-06\n", + " 24\t4.0195e-13\t-1.19818347e-06\t4.70112506e-07\t-3.64018798e-07\t-2.2084e-06\n", + " 25\t8.5330e-15\t-3.64018798e-07\t4.70112506e-07\t5.30385125e-08\t3.2177e-07\n", + "Dichotomic search with safeguarded interpolation (0.3000)\n", + "feval\trel gap\t\tx_-\t\tx_+\t\tx\t\tf'(x)\n", + " 3\t4.6647e-01\t-5.00000000e-01\t3.00000000e+00\t1.13641217e+00\t3.2205e-01\n", + " 4\t4.0396e-01\t-5.00000000e-01\t1.13641217e+00\t6.45488518e-01\t3.0611e-01\n", + " 5\t1.8075e-01\t-5.00000000e-01\t6.45488518e-01\t3.01841963e-01\t8.8071e-01\n", + " 6\t1.0638e-02\t-5.00000000e-01\t3.01841963e-01\t6.12893739e-02\t3.3443e-01\n", + " 7\t1.0638e-02\t-5.00000000e-01\t6.12893739e-02\t-1.07097438e-01\t-7.4538e-01\n", + " 8\t2.5086e-04\t-1.07097438e-01\t6.12893739e-02\t9.13789778e-03\t5.4637e-02\n", + " 9\t2.5086e-04\t-1.07097438e-01\t9.13789778e-03\t-2.57327030e-02\t-1.6225e-01\n", + " 10\t5.3189e-06\t-2.57327030e-02\t9.13789778e-03\t-1.32328247e-03\t-8.0445e-03\n", + " 11\t5.3189e-06\t-1.32328247e-03\t9.13789778e-03\t1.81507161e-03\t1.0980e-02\n", + " 12\t4.3014e-11\t-1.32328247e-03\t1.81507161e-03\t3.76571780e-06\t2.2845e-05\n", + " 13\t4.3014e-11\t-1.32328247e-03\t3.76571780e-06\t-3.94348738e-04\t-2.3939e-03\n", + " 14\t4.3014e-11\t-3.94348738e-04\t3.76571780e-06\t-1.15668619e-04\t-7.0185e-04\n", + " 15\t4.3014e-11\t-1.15668619e-04\t3.76571780e-06\t-3.20645832e-05\t-1.9453e-04\n", + " 16\t4.3014e-11\t-3.20645832e-05\t3.76571780e-06\t-6.98337251e-06\t-4.2366e-05\n", + " 17\t5.1438e-21\t-6.98337251e-06\t3.76571780e-06\t4.11796824e-11\t2.4982e-10\n", + "Dichotomic search with safeguarded interpolation (0.0500)\n", + "feval\trel gap\t\tx_-\t\tx_+\t\tx\t\tf'(x)\n", + " 3\t4.6647e-01\t-5.00000000e-01\t3.00000000e+00\t1.13641217e+00\t3.2205e-01\n", + " 4\t4.3846e-01\t-5.00000000e-01\t1.13641217e+00\t9.84326389e-01\t7.5871e-02\n", + " 5\t4.3486e-01\t-5.00000000e-01\t9.84326389e-01\t9.10110069e-01\t3.0546e-02\n", + " 6\t4.3266e-01\t-5.00000000e-01\t9.10110069e-01\t8.39604566e-01\t4.0287e-02\n", + " 7\t4.2833e-01\t-5.00000000e-01\t8.39604566e-01\t7.72624338e-01\t9.6308e-02\n", + " 8\t4.1949e-01\t-5.00000000e-01\t7.72624338e-01\t7.08993121e-01\t1.8704e-01\n", + " 9\t4.0260e-01\t-5.00000000e-01\t7.08993121e-01\t6.41091591e-01\t3.1515e-01\n", + " 10\t3.5812e-01\t-5.00000000e-01\t6.41091591e-01\t5.37106669e-01\t5.4285e-01\n", + " 11\t2.5177e-01\t-5.00000000e-01\t5.37106669e-01\t3.84368359e-01\t8.2455e-01\n", + " 12\t9.4246e-02\t-5.00000000e-01\t3.84368359e-01\t2.00583037e-01\t7.9589e-01\n", + " 13\t9.8947e-03\t-5.00000000e-01\t2.00583037e-01\t5.90289844e-02\t3.2347e-01\n", + " 14\t1.4196e-04\t-5.00000000e-01\t5.90289844e-02\t6.86585262e-03\t4.1202e-02\n", + " 15\t1.4196e-04\t-5.00000000e-01\t6.86585262e-03\t-1.84774400e-02\t-1.1528e-01\n", + " 16\t1.1298e-07\t-1.84774400e-02\t6.86585262e-03\t1.93016015e-04\t1.1706e-03\n", + " 17\t1.1298e-07\t-1.84774400e-02\t1.93016015e-04\t-7.40506786e-04\t-4.4976e-03\n", + " 18\t1.5154e-13\t-7.40506786e-04\t1.93016015e-04\t2.23515295e-07\t1.3560e-06\n", + " 19\t1.5154e-13\t-7.40506786e-04\t2.23515295e-07\t-3.68129998e-05\t-2.2335e-04\n", + " 20\t1.5154e-13\t-3.68129998e-05\t2.23515295e-07\t-1.62831046e-06\t-9.8784e-06\n", + " 21\t9.8526e-25\t-1.62831046e-06\t2.23515295e-07\t5.69923312e-13\t3.4575e-12\n", + "Univariate Newton's Method\n", + "feval\trel gap\t\tx\t\tf(x)\t\tf'(x)\t\tf''(x)\n", + " 1\t8.9644e-01\t-5.00000000e-01\t8.9644e-01\t-3.1432e+00\t2.8378e-01\n", + " 2\t8.9644e-01\t1.05758636e+01\t5.9574e+07\t6.5971e+07\t6.5518e+07\n", + " 3\t8.9644e-01\t9.56895366e+00\t1.8219e+07\t2.2809e+07\t2.5586e+07\n", + " 4\t8.9644e-01\t8.67749933e+00\t5.5618e+06\t7.8820e+06\t9.9974e+06\n", + " 5\t8.9644e-01\t7.88908699e+00\t1.6941e+06\t2.7220e+06\t3.9088e+06\n", + " 6\t8.9644e-01\t7.19272084e+00\t5.1457e+05\t9.3922e+05\t1.5295e+06\n", + " 7\t8.9644e-01\t6.57866301e+00\t1.5577e+05\t3.2375e+05\t5.9911e+05\n", + " 8\t8.9644e-01\t6.03828653e+00\t4.6968e+04\t1.1145e+05\t2.3496e+05\n", + " 9\t8.9644e-01\t5.56393556e+00\t1.4098e+04\t3.8312e+04\t9.2284e+04\n", + " 10\t8.9644e-01\t5.14878422e+00\t4.2138e+03\t1.3146e+04\t3.6305e+04\n", + " 11\t8.9644e-01\t4.78667921e+00\t1.2567e+03\t4.5027e+03\t1.4306e+04\n", + " 12\t8.9644e-01\t4.47193861e+00\t3.7665e+02\t1.5400e+03\t5.6432e+03\n", + " 13\t8.9644e-01\t4.19905074e+00\t1.1571e+02\t5.2675e+02\t2.2235e+03\n", + " 14\t8.9644e-01\t3.96214954e+00\t3.8146e+01\t1.8107e+02\t8.6974e+02\n", + " 15\t8.9644e-01\t3.75395648e+00\t1.4626e+01\t6.3360e+01\t3.3222e+02\n", + " 16\t8.9644e-01\t3.56323926e+00\t7.0040e+00\t2.3292e+01\t1.1868e+02\n", + " 17\t8.9644e-01\t3.36697475e+00\t4.0363e+00\t9.6173e+00\t3.5828e+01\n", + " 18\t8.9644e-01\t3.09854269e+00\t2.2767e+00\t4.5568e+00\t1.0606e+01\n", + " 19\t8.9644e-01\t2.66890891e+00\t1.1804e+00\t7.6520e-01\t6.7669e+00\n", + " 20\t8.9644e-01\t2.55582999e+00\t1.1328e+00\t1.1808e-01\t4.5954e+00\n", + " 21\t8.9644e-01\t2.53013577e+00\t1.1312e+00\t7.0288e-03\t4.0462e+00\n", + " 22\t8.9644e-01\t2.52839863e+00\t1.1312e+00\t3.2604e-05\t4.0087e+00\n", + " 23\t8.9644e-01\t2.52839050e+00\t1.1312e+00\t7.1395e-10\t4.0085e+00\n" + ] + }, + { + "data": { + "image/png": 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"metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"oneDTestFunctions.jl\")\n", + "include(\"GRS.jl\")\n", + "include(\"DIS.jl\")\n", + "include(\"UNM.jl\")\n", + "\n", + "plt = plot(yscale = :log,\n", + " xlims=(0, 35),\n", + " ylims=(1e-15, Inf),\n", + " guidefontsize=16)\n", + "\n", + "GRS(oneDTestFunctions()[1], plt=plt, plotatend=false, Plotg=1)\n", + "DIS(oneDTestFunctions()[1], plt=plt, plotatend=false, Plotg=1)\n", + "DIS(oneDTestFunctions()[1]; sfgrd=0.49999, plt=plt, plotatend=false, Plotg=1)\n", + "DIS(oneDTestFunctions()[1]; sfgrd=0.30, plt=plt, plotatend=false, Plotg=1)\n", + "DIS(oneDTestFunctions()[1]; sfgrd=0.05, plt=plt, plotatend=false, Plotg=1)\n", + "UNM(oneDTestFunctions()[1]; plt=plt, plotatend=false, Plotg=1)\n", + "display(plt)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-12/oneDTestFunctions.jl b/10-12/oneDTestFunctions.jl new file mode 100644 index 0000000..94fec57 --- /dev/null +++ b/10-12/oneDTestFunctions.jl @@ -0,0 +1,65 @@ +using LinearAlgebra + +function custom(x::Union{Nothing, Number})::Tuple{Number, Number, Number, Union{Nothing, Tuple{Number, Number}}} + # custom 10-th degree polynomial + # f = @(x) 91 * x^2 / 30 - 19 * x^3 / 6 - 54 * x^4 / 25 ... + # + 93 * x^5 / 23 - 23 * x^6 / 36 - 121 * x^7 / 93 ... + # + 72 * x^8 / 91 - 13 * x^9 / 74 + 9 * x^10 / 640 + # + # f'(x) + # (9*x^9)/64 - (117*x^8)/74 + (576*x^7)/91 - (847*x^6)/93 + # - (23*x^5)/6 + (465*x^4)/23 - (216*x^3)/25 - (19*x^2)/2 + (91*x)/15 + # + # f''(x) + # (81*x^8)/64 - (468*x^7)/37 + (576*x^6)/13 - (1694*x^5)/31 + # - (115*x^4)/6 + (1860*x^3)/23 - (648*x^2)/25 - 19*x + 91/15 + + if x == nothing # informative call + return (0, 0, 0, (-0.5, 3)) + else + f(x) = 91 * x^2 / 30 - 19 * x^3 / 6 - 54 * x^4 / 25 + + 93 * x^5 / 23 - 23 * x^6 / 36 - 121 * x^7 / 93 + + 72 * x^8 / 91 - 13 * x^9 / 74 + 9 * x^10 / 640 + Df(x) = 91 * x / 15 - 19 * x^2 / 2 - 216 * x^3 / 25 + 465 * x^4 / 23 - + 23 * x^5 / 6 - 847 * x^6 / 93 + 576 * x^7 / 91 - + 117 * x^8 / 74 + 9 * x^9 / 64 + DDf(x) = 91 / 15 - 19 * x - 648 * x^2 / 25 + 1860 * x^3 / 23 - + 115 * x^4 / 6 - 1694 * x^5 / 31 + 576 * x^6 / 13 - + 468 * x^7 / 37 + 81 * x^8 / 64 + return (f(x), Df(x), DDf(x), nothing) + end +end # custom + + + +function genericquad(Q::Matrix, q::Vector, x::Nothing)::Tuple{Number, Nothing, Nothing, Tuple{Vector, Vector}} + # generic quadratic function f(x) = x' * Q * x / 2 + q' * x + # informative call + + if minimum(eigvals(Q)) > 1e-14 + xStar = Q \ -q + v = 0.5 * dot(xStar', Q, xStar) + dot(q', xStar) + else + v = -Inf + end + return (v, nothing, nothing, ([-0.5, -0.5], [3, 3])) +end # genericquad + +function genericquad(Q::Matrix, q::Vector, x::Vector)::Tuple{Number, Vector, Matrix, Nothing} + # generic quadratic function f(x) = x' * Q * x / 2 + q' * x + + if !(size(x) == (2,)) + throw(ArgumentError("genericquad: x is of wrong size")) + end + v = 0.5 * dot(x', Q, x) + dot(q', x) # f(x) + v2 = Q * x + q # \nabla f(x) + v3 = Q # \nabla^2 f(x) + + return (v, v2, v3, nothing) +end # genericquad + + + +function oneDTestFunctions()::Array{Any} + return [custom, x -> genericquad([6 -2;-2 6], [10; 5], x)] +end \ No newline at end of file diff --git a/10-12/oneDTestFunctions.m b/10-12/oneDTestFunctions.m new file mode 100644 index 0000000..0d101ff --- /dev/null +++ b/10-12/oneDTestFunctions.m @@ -0,0 +1,128 @@ +function TF = oneDTestFunctions() + +%function TF = oneDTestFunctions() +% +% Produces a cell array of function handlers, useful to test unconstrained +% one-dimensional optimization algorithms. +% +% Each function in the array has the following interface: +% +% [ v , varargout ] = f( x ) +% +% Input: +% +% - x is either a scalar real denoting the input of f(), or [] (empty). +% +% Output: +% +% - v (real, scalar): if x == [] this is the best known lower bound on +% the global optimum of f() on the standard interval in which f() is +% supposed to be minimised (see next). If x ~= [] then v = f(x). +% +% - g (real, either scalar or a [ 1 x 2 ] matrix denoting an interval) is +% the first optional argument. This also depends on x. if x == [] then +% g is a [ 1 x 2 ] matrix denoting the standard interval in which f() +% is supposed to be minimised (into which v is the minimum). If x ~= [] +% then g is a scalar containing the dervative g = f'(x) (or a +% subgradient of f() in x if f() is not differentiable at x). +% +% - H (real, scalar) is the second optional argument. This must only be +% specified if x ~= [], and it is the second derivative h = f''(x). +% If no such information is available, the function throws error. +% +% The current list of functions is the following: +% +% 1 Taylored nasty 10-th degree polynomial with a local minimum in 0 +% (with value 0), a local minimum around -1, a saddle point around +% +1, a local maximum around +2 and another local minimum around +3. +% Thought to be plotted in [ -1.2 , 3.1 ], and minimised in +% [ -0.5 , 3 ] (the global minimim there being 0). Note that, by +% taking [ -1 , 3 ] instead, blind dycothomic search would hit jackpot +% in two iterations by sheer luck. +% +%{ + ======================================= + Author: Antonio Frangioni + Date: 08-11-18 + Version 1.01 + Copyright Antonio Frangioni + ======================================= +%} + +TF = cell( 1 , 1 ); +%TF{ 1 } = @(x) genericquad( [ 6 -2 ; -2 6 ] , [ 10 ; 5 ] , x ); +TF{ 1 } = @custom; + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +% function [ v , varargout ] = genericquad( Q , q , x ) +% % generic quadratic function f(x) = x' * Q * x / 2 + q' * x +% +% if isempty( x ) % informative call +% if min( eig( Q ) ) > 1e-14 +% xStar = Q \ -q; +% v = 0.5 * xStar' * Q * xStar + q' * xStar; +% else +% v = - Inf; +% end +% if nargout > 1 +% varargout{ 1 } = [ 0 ; 0 ]; +% end +% else +% if ~ isequal( size( x ) , [ 2 1 ] ) +% error( 'genericquad: x is of wrong size' ); +% end +% v = 0.5 * x' * Q * x + q' * x; % f(x) +% if nargout > 1 +% varargout{ 1 } = Q * x + q; % \nabla f(x) +% if nargout > 2 +% varargout{ 2 } = Q; % \nabla^2 f(x) +% end +% end +% end +% end % genericquad + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [ v , varargout ] = custom( x ) +% custom 10-th degree polynomial +% syms x +% f = @(x) 91 * x^2 / 30 - 19 * x^3 / 6 - 54 * x^4 / 25 ... +% + 93 * x^5 / 23 - 23 * x^6 / 36 - 121 * x^7 / 93 ... +% + 72 * x^8 / 91 - 13 * x^9 / 74 + 9 * x^10 / 640 +% +% diff( f , x ) +% 9*x^9)/64 - (117*x^8)/74 + (576*x^7)/91 - (847*x^6)/93 +% - (23*x^5)/6 + (465*x^4)/23 - (216*x^3)/25 - (19*x^2)/2 + (91*x)/15 +% +% diff( f , x , 2 ) +% (81*x^8)/64 - (468*x^7)/37 + (576*x^6)/13 - (1694*x^5)/31 +% - (115*x^4)/6 + (1860*x^3)/23 - (648*x^2)/25 - 19*x + 91/15 + +if isempty( x ) % informative call + v = 0; + if nargout > 1 + varargout{ 1 } = [ -0.5 3 ]; + end +else + v = 91 * x^2 / 30 - 19 * x^3 / 6 - 54 * x^4 / 25 ... + + 93 * x^5 / 23 - 23 * x^6 / 36 - 121 * x^7 / 93 ... + + 72 * x^8 / 91 - 13 * x^9 / 74 + 9 * x^10 / 640; + if nargout > 1 + g = 91 * x / 15 - 19 * x^2 / 2 - 216 * x^3 / 25 + 465 * x^4 / 23 ... + - 23 * x^5 / 6 - 847 * x^6 / 93 + 576 * x^7 / 91 ... + - 117 * x^8 / 74 + 9 * x^9 / 64; + varargout{ 1 } = g; % f'(x) + if nargout > 2 + h = 91 / 15 - 19 * x - 648 * x^2 / 25 + 1860 * x^3 / 23 ... + - 115 * x^4 / 6 - 1694 * x^5 / 31 + 576 * x^6 / 13 ... + - 468 * x^7 / 37 + 81 * x^8 / 64; + varargout{ 2 } = h; % f''(x) + end + end +end +end % custom + +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +end diff --git a/10-18/lesson.ipynb b/10-18/lesson.ipynb new file mode 100644 index 0000000..77a3279 --- /dev/null +++ b/10-18/lesson.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "f82ee212-d46c-43bb-a0d1-a68f3cd1dfa6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5×5 Matrix{Float64}:\n", + " 1.87346 0.927913 2.52746 -0.482284 0.513368\n", + " 0.927913 3.18765 0.958831 -0.350004 2.53703\n", + " 2.52746 0.958831 5.76174 -0.179455 -0.781054\n", + " -0.482284 -0.350004 -0.179455 1.03152 0.015535\n", + " 0.513368 2.53703 -0.781054 0.015535 5.93971" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "5×1 Matrix{Float64}:\n", + " -1.6078855163050978\n", + " -0.23218159505096736\n", + " -0.6715758819689612\n", + " -2.3296877597548655\n", + " 1.2016661898413077" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "using LinearAlgebra\n", + "\n", + "A = randn(5,5)\n", + "Q = A' * A # positive definite\n", + "\n", + "v = randn(5,1)\n", + "\n", + "display(Q)\n", + "display(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "74a7b8e6-03b2-4799-abd2-6cdb7a644259", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5-element Vector{Float64}:\n", + " -4.9930629530914805\n", + " -0.8132084512107426\n", + " 2.243541240545553\n", + " -4.498023206367981\n", + " 1.2879883872115698" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using IterativeSolvers\n", + "\n", + "cg(Q, v) # next lesson for the real one" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-20/lesson.ipynb b/10-20/lesson.ipynb new file mode 100644 index 0000000..a4358a6 --- /dev/null +++ b/10-20/lesson.ipynb @@ -0,0 +1,22963 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0cf78481-c7a6-4df4-97da-796b4989ce26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eigenvals of Q:\n" + ] + }, + { + "data": { + "text/plain": [ + "5-element Vector{Float64}:\n", + " 0.04256575281858832\n", + " 1.2960771495578332\n", + " 2.5068176321737967\n", + " 4.762205904656057\n", + " 9.646144560835168" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"real\" solution:\n" + ] + }, + { + "data": { + "text/plain": [ + "5×1 Matrix{Float64}:\n", + " 2.3884665390055466\n", + " -1.905598226107853\n", + " -0.46982985654395737\n", + " -0.6656887117007205\n", + " 1.6808597398598424" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CG solution:\n" + ] + }, + { + "data": { + "text/plain": [ + "5-element Vector{Float64}:\n", + " 2.388466539005554\n", + " -1.9055982261078555\n", + " -0.46982985654395276\n", + " -0.6656887117007191\n", + " 1.6808597398598488" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "history:\n" + ] + }, + { + "data": { + "text/plain": [ + "Converged after 5 iterations." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "using LinearAlgebra, IterativeSolvers\n", + "\n", + "n = 5\n", + "A = randn(n,n)\n", + "# Q = A' + A + 2*n*I\n", + "Q = A' * A\n", + "println(\"eigenvals of Q:\")\n", + "eigvals(Q) |> display\n", + "v = rand(5,1)\n", + "# i want something that solves my linear equation problem\n", + "println(\"\\\"real\\\" solution:\")\n", + "Q \\ v |> display\n", + "\n", + "# like\n", + "(x, ch) = cg(Q, v, log=true)\n", + "println(\"CG solution:\")\n", + "x |> display\n", + "println(\"history:\")\n", + "ch |> display" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5a2417f0-266b-4741-b565-056c012a66ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.373315759902148e-14" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# error\n", + "norm(Q * x - v) / norm(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "0ce75dab-108e-4a5a-bae4-6f93db7e6fac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using Plots\n", + "plt = plot(ch.data[:resnorm], yaxis=:log)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "86d17dad-d37d-4fe6-9afb-fbd2ae16edca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "# height, length, density\n", + "A = sprandn(n, n, 2.5/n)\n", + "A = 0.5 * (A' + A)\n", + "spy(A) |> display\n", + "println(\"non zero entries of A: $(nnz(A))\")\n", + "println(\"expected: $(5/n * n * n)\")\n", + "\n", + "## some eigenvalues are negative\n", + "minimum(eigvals(Array(A))) |> display\n", + "maximum(eigvals(Array(A))) |> display\n", + "\n", + "Q = (-minimum(eigvals(Array(A))) + .01) .* I + A\n", + "# shifted to make them all positive\n", + "minimum(eigvals(Array(Q))) |> display\n", + "maximum(eigvals(Array(Q))) |> display\n", + "\n", + "spy(Q) |> display" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9bf00ddc-9b1c-4d5a-9970-2b74e2896c77", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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yaxis=:log) |> display\n", + "ch" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33b88a12-b888-41c8-be93-4275192545d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.027815 seconds (22.01 k allocations: 9.180 MiB, 84.00% compilation time)\n", + " 0.683799 seconds (8.78 k allocations: 8.386 MiB, 0.45% compilation time)\n" + ] + } + ], + "source": [ + "@time for t=1:1000; Q*v; end; # much faster than with full matrix\n", + "\n", + "Q = Array(Q)\n", + "\n", + "@time for t=1:1000; Q*v; end;" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "45c17fde-d328-477a-8772-56d6b8d6684e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "householder_vector (generic function with 2 methods)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function householder_vector(x::Vector{<:AbstractFloat})::Tuple{Vector, Real}\n", + " # returns the normalized vector u such that H*x is a multiple of e_1\n", + "\n", + " s = norm(x)\n", + " v = deepcopy(x)\n", + " v[1] -= s\n", + " u = v ./ norm(v)\n", + " return u, s\n", + "end\n", + "\n", + "function householder_vector(x::Matrix{<:AbstractFloat})::Tuple{Matrix, Real}\n", + " # returns the normalized vector u such that H*x is a multiple of e_1\n", + "\n", + " s = norm(x)\n", + " v = deepcopy(x)\n", + " v[1] -= s\n", + " u = v ./ norm(v)\n", + " return u, s\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "974a3c12-3b2e-41f9-ad89-d24bd2c4ae4c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4×4 Matrix{Float64}:\n", + " -0.228426 -0.742329 -0.170779 -0.606303\n", + " -0.742329 0.551416 -0.1032 -0.366385\n", + " -0.170779 -0.1032 0.976258 -0.0842898\n", + " -0.606303 -0.366385 -0.0842898 0.700752" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1.8097651944067387e-16" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "4×1 Matrix{Float64}:\n", + " -1.7835733314400501\n", + " 1.8472620051509155e-16\n", + " 3.0882187722193e-17\n", + " 6.041947842933401e-17" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = randn(4,1)\n", + "(u, s) = householder_vector(x)\n", + "\n", + "H = I - 2*u*u'\n", + "display(H)\n", + "\n", + "norm(H' * H - I) |> display # very close to the identity matrix\n", + "\n", + "H*x |> display # very close to e_1 times something" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "035cecb6-62b5-4eb0-ab16-945a18cccadb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4×1 Matrix{Float64}:\n", + " 0.7837173833516905\n", + " 0.47359494289505655\n", + " 0.10895432877426844\n", + " 0.38681241880006406" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "-1.7835733314400501" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(u)\n", + "display(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0db14574-9c5e-40c2-a7b2-d73facc0467e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4-element Vector{Float64}:\n", + " 1.0\n", + " 5.0e-13\n", + " 5.0e-13\n", + " 5.0e-13" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "4×4 Matrix{Float64}:\n", + " -1.0 -1.0e-12 -1.0e-12 -1.0e-12\n", + " -1.0e-12 1.0 -5.0e-25 -5.0e-25\n", + " -1.0e-12 -5.0e-25 1.0 -5.0e-25\n", + " -1.0e-12 -5.0e-25 -5.0e-25 1.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1.5811388300841895e-36" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "4-element Vector{Float64}:\n", + " -1.0e6\n", + " -9.999999999999999e-31\n", + " -4.9999999999999995e-31\n", + " 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1.1180339887498946e-36" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# bad if norm is close to the first entry\n", + "x = [1e6; 1e-6; 1e-6; 1e-6]\n", + "\n", + "(u, s) = householder_vector(x)\n", + "\n", + "display(u) # first entry is 0 but should be 3e-6\n", + "\n", + "H = I - 2*u*u'\n", + "display(H)\n", + "\n", + "norm(H' * H - I) |> display\n", + "\n", + "H*x |> display\n", + "\n", + "# the relative error is:\n", + "norm(H*x - [s; 0; 0; 0]) / norm([s; 0; 0; 0])\n", + "# too large (10^-12)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "098a554f-defe-4cb1-9bda-17c33732732d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "householder_vector (generic function with 2 methods)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function householder_vector(x::Vector{<:AbstractFloat})::Tuple{Vector, Real}\n", + " # returns the normalized vector u such that H*x is a multiple of e_1\n", + "\n", + " s = norm(x)\n", + " if x[1] ≥ 0\n", + " s = -s\n", + " end\n", + " v = deepcopy(x)\n", + " v[1] -= s\n", + " u = v ./ norm(v)\n", + " return u, s\n", + "end\n", + "\n", + "function householder_vector(x::Matrix{<:AbstractFloat})::Tuple{Matrix, Real}\n", + " # returns the normalized vector u such that H*x is a multiple of e_1\n", + "\n", + " s = norm(x)\n", + " if x[1] ≥ 0\n", + " s = -s\n", + " end\n", + " v = deepcopy(x)\n", + " v[1] -= s\n", + " u = v ./ norm(v)\n", + " return u, s\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "540ef886-8074-4d8f-a914-6214e8b2a79b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4-element Vector{Float64}:\n", + " -1.0e6\n", + " -9.999999999999999e-31\n", + " -4.9999999999999995e-31\n", + " 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "4-element Vector{Float64}:\n", + " -1.0e6\n", + " 0.0\n", + " 0.0\n", + " 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(u, s) = householder_vector(x)\n", + "\n", + "H = I - 2*u*u'\n", + "\n", + "H * x |> display # much better\n", + "\n", + "y = x - u*(2*u'*x) # even better\n", + "display(y)\n", + "\n", + "### for numerical stability the vector that doesnt cause cancellation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56b5444f-c1d6-40ab-a3a1-1237392d15a9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-25/lesson.ipynb b/10-25/lesson.ipynb new file mode 100644 index 0000000..f4dd329 --- /dev/null +++ b/10-25/lesson.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8109ed55-2f25-4467-a122-425bc46487b3", + "metadata": {}, + "outputs": [], + "source": [ + "using LinearAlgebra, Plots, IterativeSolvers" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "965b8333-7efd-4d9a-a348-c44cdca2d831", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$e^{x} \\sin{\\left(y \\right)}$" + ], + "text/plain": [ + " x \n", + "ℯ ⋅sin(y)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$e^{x} \\cos{\\left(y \\right)}$" + ], + "text/plain": [ + " x \n", + "ℯ ⋅cos(y)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "using SymPy\n", + "@vars x y\n", + "\n", + "f(x, y) = ℯ^x * sin(y)\n", + "\n", + "diff(f(x, y), x) |> display\n", + "diff(f(x, y), y) |> display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e8f8674-1e48-4903-8240-cfcbf01329b5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-26/lesson.ipynb b/10-26/lesson.ipynb new file mode 100644 index 0000000..dfcc997 --- /dev/null +++ b/10-26/lesson.ipynb @@ -0,0 +1,1596 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ec9ea22e-1946-4601-9ddd-ba6b9243857a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "gmq (generic function with 1 method)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "include(\"../10-04/GMQ.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "107c678a-21ff-4877-96da-d8c3b34cfb5d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-14.843750953674316" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient method for quadratic functions (fixed stepsize)\n", + "iter\tf(x)\t\t\t||g||\t\tgap\t\trate\n", + "\n", + " 1\t0.00000000e+00\t\t1.1180e+01\t1.0000e+00\t\t\n", + " 2\t-1.31944444e+01\t\t3.7268e+00\t1.1111e-01\t1.1111e-01\n", + " 3\t-1.46604938e+01\t\t1.2423e+00\t1.2346e-02\t1.1111e-01\n", + " 4\t-1.48233882e+01\t\t4.1409e-01\t1.3718e-03\t1.1112e-01\n", + " 5\t-1.48414876e+01\t\t1.3803e-01\t1.5248e-04\t1.1115e-01\n", + " 6\t-1.48434986e+01\t\t4.6010e-02\t1.6999e-05\t1.1149e-01\n", + " 7\t-1.48437221e+01\t\t1.5337e-02\t1.9459e-06\t1.1447e-01\n", + " 8\t-1.48437469e+01\t\t5.1122e-03\t2.7332e-07\t1.4046e-01\n", + " 9\t-1.48437497e+01\t\t1.7041e-03\t8.7478e-08\t3.2005e-01\n", + " 10\t-1.48437500e+01\t\t5.6802e-04\t6.6829e-08\t7.6395e-01\n", + " 11\t-1.48437500e+01\t\t1.8934e-04\t6.4534e-08\t9.6567e-01\n", + " 12\t-1.48437500e+01\t\t6.3113e-05\t6.4279e-08\t9.9605e-01\n", + " 13\t-1.48437500e+01\t\t2.1038e-05\t6.4251e-08\t9.9956e-01\n", + " 14\t-1.48437500e+01\t\t7.0126e-06\t6.4248e-08\t9.9995e-01\n", + " 15\t-1.48437500e+01\t\t2.3375e-06\t6.4248e-08\t9.9999e-01\n", + " 16\t-1.48437500e+01\t\t7.7918e-07\t6.4248e-08\t1.0000e+00" + ] + }, + { + "data": { + "image/png": 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"metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Q = Float32[6 -2; -2 6]\n", + "q = Float32[10, 5]\n", + "xStar = Q \\ -q\n", + "v = 0.5 * dot(xStar, Q, xStar) + q' * xStar\n", + "v |> display\n", + "gmq(Q, q, fStar=v, alpha=2/(8+4), Plotf=1, printing=true) # 16 iterations instead of 12 but useful for higher dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "094a4e0f-dfd8-4cde-a2f1-3ca1e4cccef4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-3.406252861022949" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient method for quadratic functions (optimal stepsize)\n", + "iter\tf(x)\t\t\t||g||\t\tgap\t\trate\t\talpha\n", + "\n", + " 1\t0.00000000e+00\t\t1.1180e+01\t1.0000e+00\t\t\t5.55e-03\n", + " 2\t-3.46836848e-01\t\t3.6854e+00\t8.9818e-01\t8.9818e-01\t4.59e-02\n", + " 3\t-6.58357498e-01\t\t1.0042e+01\t8.0672e-01\t8.9818e-01\t5.55e-03\n", + " 4\t-9.38157975e-01\t\t3.3102e+00\t7.2458e-01\t8.9818e-01\t4.59e-02\n", + " 5\t-1.18946814e+00\t\t9.0194e+00\t6.5080e-01\t8.9818e-01\t5.55e-03\n", + " 6\t-1.41518899e+00\t\t2.9731e+00\t5.8453e-01\t8.9818e-01\t4.59e-02\n", + " 7\t-1.61792611e+00\t\t8.1010e+00\t5.2501e-01\t8.9818e-01\t5.55e-03\n", + " 8\t-1.80001980e+00\t\t2.6704e+00\t4.7155e-01\t8.9818e-01\t4.59e-02\n", + " 9\t-1.96357204e+00\t\t7.2761e+00\t4.2354e-01\t8.9818e-01\t5.55e-03\n", + " 10\t-2.11047079e+00\t\t2.3985e+00\t3.8041e-01\t8.9818e-01\t4.59e-02\n", + " 11\t-2.24241178e+00\t\t6.5353e+00\t3.4168e-01\t8.9818e-01\t5.55e-03\n", + " 12\t-2.36091805e+00\t\t2.1542e+00\t3.0689e-01\t8.9818e-01\t4.59e-02\n", + " 13\t-2.46735757e+00\t\t5.8698e+00\t2.7564e-01\t8.9818e-01\t5.55e-03\n", + " 14\t-2.56295904e+00\t\t1.9349e+00\t2.4757e-01\t8.9818e-01\t4.59e-02\n", + " 15\t-2.64882601e+00\t\t5.2721e+00\t2.2236e-01\t8.9818e-01\t5.55e-03\n", + " 16\t-2.72594970e+00\t\t1.7379e+00\t1.9972e-01\t8.9818e-01\t4.59e-02\n", + " 17\t-2.79522036e+00\t\t4.7353e+00\t1.7939e-01\t8.9818e-01\t5.55e-03\n", + " 18\t-2.85743764e+00\t\t1.5609e+00\t1.6112e-01\t8.9818e-01\t4.59e-02\n", + " 19\t-2.91331972e+00\t\t4.2531e+00\t1.4471e-01\t8.9818e-01\t5.55e-03\n", + " 20\t-2.96351169e+00\t\t1.4020e+00\t1.2998e-01\t8.9818e-01\t4.59e-02\n", + " 21\t-3.00859292e+00\t\t3.8201e+00\t1.1674e-01\t8.9818e-01\t5.55e-03\n", + " 22\t-3.04908382e+00\t\t1.2592e+00\t1.0486e-01\t8.9818e-01\t4.59e-02\n", + " 23\t-3.08545179e+00\t\t3.4311e+00\t9.4180e-02\t8.9818e-01\t5.55e-03\n", + " 24\t-3.11811664e+00\t\t1.1310e+00\t8.4590e-02\t8.9818e-01\t4.59e-02\n", + " 25\t-3.14745543e+00\t\t3.0817e+00\t7.5977e-02\t8.9818e-01\t5.55e-03\n", + " 26\t-3.17380684e+00\t\t1.0158e+00\t6.8241e-02\t8.9818e-01\t4.59e-02\n", + " 27\t-3.19747506e+00\t\t2.7679e+00\t6.1293e-02\t8.9818e-01\t5.55e-03\n", + " 28\t-3.21873329e+00\t\t9.1240e-01\t5.5052e-02\t8.9818e-01\t4.59e-02\n", + " 29\t-3.23782693e+00\t\t2.4861e+00\t4.9446e-02\t8.9818e-01\t5.55e-03\n", + " 30\t-3.25497638e+00\t\t8.1950e-01\t4.4411e-02\t8.9818e-01\t4.59e-02\n", + " 31\t-3.27037961e+00\t\t2.2330e+00\t3.9889e-02\t8.9818e-01\t5.55e-03\n", + " 32\t-3.28421443e+00\t\t7.3606e-01\t3.5828e-02\t8.9818e-01\t4.59e-02\n", + " 33\t-3.29664054e+00\t\t2.0056e+00\t3.2180e-02\t8.9818e-01\t5.55e-03\n", + " 34\t-3.30780138e+00\t\t6.6111e-01\t2.8903e-02\t8.9818e-01\t4.59e-02\n", + " 35\t-3.31782578e+00\t\t1.8014e+00\t2.5960e-02\t8.9818e-01\t5.55e-03\n", + " 36\t-3.32682946e+00\t\t5.9379e-01\t2.3317e-02\t8.9818e-01\t4.59e-02\n", + " 37\t-3.33491635e+00\t\t1.6179e+00\t2.0943e-02\t8.9818e-01\t5.55e-03\n", + " 38\t-3.34217980e+00\t\t5.3333e-01\t1.8810e-02\t8.9818e-01\t4.59e-02\n", + " 39\t-3.34870366e+00\t\t1.4532e+00\t1.6895e-02\t8.9818e-01\t5.55e-03\n", + " 40\t-3.35456324e+00\t\t4.7902e-01\t1.5175e-02\t8.9818e-01\t4.59e-02\n", + " 41\t-3.35982618e+00\t\t1.3052e+00\t1.3630e-02\t8.9818e-01\t5.55e-03\n", + " 42\t-3.36455322e+00\t\t4.3025e-01\t1.2242e-02\t8.9818e-01\t4.59e-02\n", + " 43\t-3.36879894e+00\t\t1.1723e+00\t1.0996e-02\t8.9818e-01\t5.55e-03\n", + " 44\t-3.37261234e+00\t\t3.8644e-01\t9.8761e-03\t8.9818e-01\t4.59e-02\n", + " 45\t-3.37603745e+00\t\t1.0530e+00\t8.8706e-03\t8.9818e-01\t5.55e-03\n", + " 46\t-3.37911380e+00\t\t3.4709e-01\t7.9674e-03\t8.9819e-01\t4.59e-02\n", + " 47\t-3.38187691e+00\t\t9.4574e-01\t7.1562e-03\t8.9819e-01\t5.55e-03\n", + " 48\t-3.38435867e+00\t\t3.1175e-01\t6.4276e-03\t8.9819e-01\t4.59e-02\n", + " 49\t-3.38658772e+00\t\t8.4944e-01\t5.7732e-03\t8.9819e-01\t5.55e-03\n", + " 50\t-3.38858981e+00\t\t2.8000e-01\t5.1855e-03\t8.9819e-01\t4.59e-02\n", + " 51\t-3.39038803e+00\t\t7.6295e-01\t4.6576e-03\t8.9819e-01\t5.55e-03\n", + " 52\t-3.39200316e+00\t\t2.5149e-01\t4.1834e-03\t8.9819e-01\t4.59e-02\n", + " 53\t-3.39345382e+00\t\t6.8526e-01\t3.7575e-03\t8.9820e-01\t5.55e-03\n", + " 54\t-3.39475678e+00\t\t2.2589e-01\t3.3750e-03\t8.9820e-01\t4.59e-02\n", + " 55\t-3.39592706e+00\t\t6.1549e-01\t3.0314e-03\t8.9820e-01\t5.55e-03\n", + " 56\t-3.39697818e+00\t\t2.0289e-01\t2.7228e-03\t8.9820e-01\t4.59e-02\n", + " 57\t-3.39792227e+00\t\t5.5282e-01\t2.4457e-03\t8.9821e-01\t5.55e-03\n", + " 58\t-3.39877023e+00\t\t1.8223e-01\t2.1967e-03\t8.9821e-01\t4.59e-02\n", + " 59\t-3.39953185e+00\t\t4.9653e-01\t1.9731e-03\t8.9822e-01\t5.55e-03\n", + " 60\t-3.40021591e+00\t\t1.6367e-01\t1.7723e-03\t8.9822e-01\t4.59e-02\n", + " 61\t-3.40083033e+00\t\t4.4597e-01\t1.5919e-03\t8.9822e-01\t5.55e-03\n", + " 62\t-3.40138218e+00\t\t1.4701e-01\t1.4299e-03\t8.9823e-01\t4.59e-02\n", + " 63\t-3.40187784e+00\t\t4.0056e-01\t1.2844e-03\t8.9824e-01\t5.55e-03\n", + " 64\t-3.40232303e+00\t\t1.3204e-01\t1.1537e-03\t8.9824e-01\t4.59e-02\n", + " 65\t-3.40272289e+00\t\t3.5977e-01\t1.0363e-03\t8.9825e-01\t5.55e-03\n", + " 66\t-3.40308203e+00\t\t1.1859e-01\t9.3089e-04\t8.9826e-01\t4.59e-02\n", + " 67\t-3.40340460e+00\t\t3.2314e-01\t8.3618e-04\t8.9827e-01\t5.55e-03\n", + " 68\t-3.40369433e+00\t\t1.0652e-01\t7.5113e-04\t8.9828e-01\t4.59e-02\n", + " 69\t-3.40395456e+00\t\t2.9023e-01\t6.7473e-04\t8.9829e-01\t5.55e-03\n", + " 70\t-3.40418829e+00\t\t9.5671e-02\t6.0611e-04\t8.9830e-01\t4.59e-02\n", + " 71\t-3.40439822e+00\t\t2.6068e-01\t5.4448e-04\t8.9832e-01\t5.55e-03\n", + " 72\t-3.40458678e+00\t\t8.5930e-02\t4.8913e-04\t8.9833e-01\t4.59e-02\n", + " 73\t-3.40475613e+00\t\t2.3414e-01\t4.3941e-04\t8.9835e-01\t5.55e-03\n", + " 74\t-3.40490824e+00\t\t7.7180e-02\t3.9475e-04\t8.9837e-01\t4.59e-02\n", + " 75\t-3.40504487e+00\t\t2.1030e-01\t3.5464e-04\t8.9839e-01\t5.55e-03\n", + " 76\t-3.40516758e+00\t\t6.9321e-02\t3.1862e-04\t8.9842e-01\t4.59e-02\n", + " 77\t-3.40527779e+00\t\t1.8888e-01\t2.8626e-04\t8.9844e-01\t5.55e-03\n", + " 78\t-3.40537679e+00\t\t6.2263e-02\t2.5720e-04\t8.9848e-01\t4.59e-02\n", + " 79\t-3.40546570e+00\t\t1.6965e-01\t2.3109e-04\t8.9851e-01\t5.55e-03\n", + " 80\t-3.40554556e+00\t\t5.5923e-02\t2.0765e-04\t8.9855e-01\t4.59e-02\n", + " 81\t-3.40561729e+00\t\t1.5238e-01\t1.8659e-04\t8.9859e-01\t5.55e-03\n", + " 82\t-3.40568171e+00\t\t5.0229e-02\t1.6768e-04\t8.9863e-01\t4.59e-02\n", + " 83\t-3.40573958e+00\t\t1.3686e-01\t1.5069e-04\t8.9869e-01\t5.55e-03\n", + " 84\t-3.40579155e+00\t\t4.5114e-02\t1.3543e-04\t8.9874e-01\t4.59e-02\n", + " 85\t-3.40583823e+00\t\t1.2293e-01\t1.2173e-04\t8.9881e-01\t5.55e-03\n", + " 86\t-3.40588016e+00\t\t4.0520e-02\t1.0942e-04\t8.9888e-01\t4.59e-02\n", + " 87\t-3.40591782e+00\t\t1.1041e-01\t9.8361e-05\t8.9896e-01\t5.55e-03\n", + " 88\t-3.40595164e+00\t\t3.6395e-02\t8.8431e-05\t8.9905e-01\t4.59e-02\n", + " 89\t-3.40598202e+00\t\t9.9167e-02\t7.9512e-05\t8.9914e-01\t5.55e-03\n", + " 90\t-3.40600931e+00\t\t3.2689e-02\t7.1501e-05\t8.9925e-01\t4.59e-02\n", + " 91\t-3.40603382e+00\t\t8.9069e-02\t6.4306e-05\t8.9937e-01\t5.55e-03\n", + " 92\t-3.40605583e+00\t\t2.9360e-02\t5.7844e-05\t8.9951e-01\t4.59e-02\n", + " 93\t-3.40607560e+00\t\t8.0000e-02\t5.2040e-05\t8.9965e-01\t5.55e-03\n", + " 94\t-3.40609336e+00\t\t2.6371e-02\t4.6826e-05\t8.9982e-01\t4.59e-02\n", + " 95\t-3.40610931e+00\t\t7.1854e-02\t4.2144e-05\t9.0000e-01\t5.55e-03\n", + " 96\t-3.40612363e+00\t\t2.3685e-02\t3.7938e-05\t9.0021e-01\t4.59e-02\n", + " 97\t-3.40613650e+00\t\t6.4538e-02\t3.4161e-05\t9.0043e-01\t5.55e-03\n", + " 98\t-3.40614806e+00\t\t2.1274e-02\t3.0768e-05\t9.0068e-01\t4.59e-02\n", + " 99\t-3.40615844e+00\t\t5.7966e-02\t2.7720e-05\t9.0096e-01\t5.55e-03\n", + " 100\t-3.40616776e+00\t\t1.9108e-02\t2.4983e-05\t9.0126e-01\t4.59e-02\n", + " 101\t-3.40617614e+00\t\t5.2064e-02\t2.2525e-05\t9.0160e-01\t5.55e-03\n", + " 102\t-3.40618366e+00\t\t1.7162e-02\t2.0317e-05\t9.0197e-01\t4.59e-02\n", + " 103\t-3.40619041e+00\t\t4.6762e-02\t1.8334e-05\t9.0239e-01\t5.55e-03\n", + " 104\t-3.40619648e+00\t\t1.5414e-02\t1.6552e-05\t9.0284e-01\t4.59e-02\n", + " 105\t-3.40620193e+00\t\t4.2001e-02\t1.4953e-05\t9.0334e-01\t5.55e-03\n", + " 106\t-3.40620682e+00\t\t1.3845e-02\t1.3516e-05\t9.0390e-01\t4.59e-02\n", + " 107\t-3.40621122e+00\t\t3.7724e-02\t1.2225e-05\t9.0450e-01\t5.55e-03\n", + " 108\t-3.40621517e+00\t\t1.2435e-02\t1.1066e-05\t9.0517e-01\t4.59e-02\n", + " 109\t-3.40621872e+00\t\t3.3883e-02\t1.0024e-05\t9.0591e-01\t5.55e-03\n", + " 110\t-3.40622190e+00\t\t1.1169e-02\t9.0892e-06\t9.0671e-01\t4.59e-02\n", + " 111\t-3.40622476e+00\t\t3.0433e-02\t8.2492e-06\t9.0759e-01\t5.55e-03\n", + " 112\t-3.40622733e+00\t\t1.0032e-02\t7.4948e-06\t9.0854e-01\t4.59e-02\n", + " 113\t-3.40622964e+00\t\t2.7334e-02\t6.8172e-06\t9.0959e-01\t5.55e-03\n", + " 114\t-3.40623171e+00\t\t9.0102e-03\t6.2085e-06\t9.1072e-01\t4.59e-02\n", + " 115\t-3.40623358e+00\t\t2.4551e-02\t5.6619e-06\t9.1195e-01\t5.55e-03\n", + " 116\t-3.40623525e+00\t\t8.0928e-03\t5.1709e-06\t9.1328e-01\t4.59e-02\n", + " 117\t-3.40623675e+00\t\t2.2051e-02\t4.7299e-06\t9.1472e-01\t5.55e-03\n", + " 118\t-3.40623810e+00\t\t7.2688e-03\t4.3338e-06\t9.1626e-01\t4.59e-02\n", + " 119\t-3.40623931e+00\t\t1.9806e-02\t3.9781e-06\t9.1791e-01\t5.55e-03\n", + " 120\t-3.40624040e+00\t\t6.5286e-03\t3.6585e-06\t9.1968e-01\t4.59e-02\n", + " 121\t-3.40624138e+00\t\t1.7789e-02\t3.3715e-06\t9.2155e-01\t5.55e-03\n", + " 122\t-3.40624225e+00\t\t5.8639e-03\t3.1137e-06\t9.2354e-01\t4.59e-02\n", + " 123\t-3.40624304e+00\t\t1.5978e-02\t2.8822e-06\t9.2564e-01\t5.55e-03\n", + " 124\t-3.40624375e+00\t\t5.2668e-03\t2.6743e-06\t9.2785e-01\t4.59e-02\n", + " 125\t-3.40624439e+00\t\t1.4351e-02\t2.4875e-06\t9.3016e-01\t5.55e-03\n", + " 126\t-3.40624496e+00\t\t4.7305e-03\t2.3197e-06\t9.3256e-01\t4.59e-02\n", + " 127\t-3.40624547e+00\t\t1.2890e-02\t2.1690e-06\t9.3504e-01\t5.55e-03\n", + " 128\t-3.40624593e+00\t\t4.2488e-03\t2.0337e-06\t9.3761e-01\t4.59e-02\n", + " 129\t-3.40624635e+00\t\t1.1577e-02\t1.9122e-06\t9.4023e-01\t5.55e-03\n", + " 130\t-3.40624672e+00\t\t3.8162e-03\t1.8030e-06\t9.4290e-01\t4.59e-02\n", + " 131\t-3.40624705e+00\t\t1.0398e-02\t1.7049e-06\t9.4561e-01\t5.55e-03\n", + " 132\t-3.40624735e+00\t\t3.4276e-03\t1.6168e-06\t9.4834e-01\t4.59e-02\n", + " 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146\t-3.40624941e+00\t\t1.6163e-03\t1.0127e-06\t9.8103e-01\t4.59e-02\n", + " 147\t-3.40624947e+00\t\t4.4041e-03\t9.9510e-07\t9.8263e-01\t5.55e-03\n", + " 148\t-3.40624953e+00\t\t1.4517e-03\t9.7930e-07\t9.8412e-01\t4.59e-02\n", + " 149\t-3.40624957e+00\t\t3.9556e-03\t9.6511e-07\t9.8551e-01\t5.55e-03\n", + " 150\t-3.40624962e+00\t\t1.3039e-03\t9.5236e-07\t9.8679e-01\t4.59e-02\n", + " 151\t-3.40624966e+00\t\t3.5528e-03\t9.4091e-07\t9.8798e-01\t5.55e-03\n", + " 152\t-3.40624969e+00\t\t1.1711e-03\t9.3063e-07\t9.8907e-01\t4.59e-02\n", + " 153\t-3.40624972e+00\t\t3.1911e-03\t9.2140e-07\t9.9008e-01\t5.55e-03\n", + " 154\t-3.40624975e+00\t\t1.0519e-03\t9.1310e-07\t9.9100e-01\t4.59e-02\n", + " 155\t-3.40624978e+00\t\t2.8662e-03\t9.0565e-07\t9.9184e-01\t5.55e-03\n", + " 156\t-3.40624980e+00\t\t9.4478e-04\t8.9896e-07\t9.9261e-01\t4.59e-02\n", + " 157\t-3.40624982e+00\t\t2.5743e-03\t8.9295e-07\t9.9331e-01\t5.55e-03\n", + " 158\t-3.40624984e+00\t\t8.4858e-04\t8.8755e-07\t9.9395e-01\t4.59e-02\n", + " 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263\t-3.40625000e+00\t\t8.6861e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 264\t-3.40625000e+00\t\t2.8632e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 265\t-3.40625000e+00\t\t7.8017e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 266\t-3.40625000e+00\t\t2.5717e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 267\t-3.40625000e+00\t\t7.0073e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 268\t-3.40625000e+00\t\t2.3098e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 269\t-3.40625000e+00\t\t6.2938e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 270\t-3.40625000e+00\t\t2.0746e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 271\t-3.40625000e+00\t\t5.6529e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 272\t-3.40625000e+00\t\t1.8634e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 273\t-3.40625000e+00\t\t5.0773e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 274\t-3.40625000e+00\t\t1.6737e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 275\t-3.40625000e+00\t\t4.5603e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 276\t-3.40625000e+00\t\t1.5032e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 277\t-3.40625000e+00\t\t4.0960e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 278\t-3.40625000e+00\t\t1.3502e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 279\t-3.40625000e+00\t\t3.6789e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 280\t-3.40625000e+00\t\t1.2127e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 281\t-3.40625000e+00\t\t3.3043e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 282\t-3.40625000e+00\t\t1.0892e-06\t8.3993e-07\t1.0000e+00\t4.59e-02\n", + " 283\t-3.40625000e+00\t\t2.9679e-06\t8.3993e-07\t1.0000e+00\t5.55e-03\n", + " 284\t-3.40625000e+00\t\t9.7830e-07\t8.3993e-07\t1.0000e+00\n" + ] + }, + { + "data": { + "image/png": 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{}, + "output_type": "execute_result" + } + ], + "source": [ + "H = Float32[1 1; 1 -1]\n", + "D = diagm([100, 1])\n", + "Q = H' * D * H\n", + "q = Float32[10, 5]\n", + "xStar = Q \\ -q\n", + "v = 0.5 * dot(xStar, Q, xStar) + q' * xStar\n", + "v |> display\n", + "gmq(Q, q, fStar=v, Plotf=1, printing=true) # 16 iterations instead of 12 but useful for higher dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "01009aed-de0a-4052-bcc7-2e59e42f3d8a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient method for quadratic functions (fixed stepsize)\n", + "iter\tf(x)\t\t\t||g||\t\tgap\t\trate\n", + "\n", + " 1\t0.00000000e+00\t\t1.1180e+01\t1.0000e+00\t\t\n", + " 2\t-1.33565337e-01\t\t1.0959e+01\t9.6079e-01\t9.6079e-01\n", + " 3\t-2.61893331e-01\t\t1.0742e+01\t9.2311e-01\t9.6079e-01\n", + " 4\t-3.85189347e-01\t\t1.0529e+01\t8.8692e-01\t9.6079e-01\n", + " 5\t-5.03650700e-01\t\t1.0321e+01\t8.5214e-01\t9.6079e-01\n", + " 6\t-6.17466965e-01\t\t1.0116e+01\t8.1873e-01\t9.6079e-01\n", + " 7\t-7.26820285e-01\t\t9.9160e+00\t7.8662e-01\t9.6079e-01\n", + " 8\t-8.31885660e-01\t\t9.7197e+00\t7.5578e-01\t9.6079e-01\n", + " 9\t-9.32831227e-01\t\t9.5272e+00\t7.2614e-01\t9.6079e-01\n", + " 10\t-1.02981853e+00\t\t9.3385e+00\t6.9767e-01\t9.6079e-01\n", + " 11\t-1.12300279e+00\t\t9.1536e+00\t6.7031e-01\t9.6079e-01\n", + " 12\t-1.21253312e+00\t\t8.9724e+00\t6.4403e-01\t9.6079e-01\n", + " 13\t-1.29855280e+00\t\t8.7947e+00\t6.1877e-01\t9.6079e-01\n", + " 14\t-1.38119949e+00\t\t8.6205e+00\t5.9451e-01\t9.6079e-01\n", + " 15\t-1.46060545e+00\t\t8.4498e+00\t5.7120e-01\t9.6079e-01\n", + " 16\t-1.53689775e+00\t\t8.2825e+00\t5.4880e-01\t9.6079e-01\n", + " 17\t-1.61019850e+00\t\t8.1185e+00\t5.2728e-01\t9.6079e-01\n", + " 18\t-1.68062499e+00\t\t7.9577e+00\t5.0661e-01\t9.6079e-01\n", + " 19\t-1.74828992e+00\t\t7.8002e+00\t4.8674e-01\t9.6079e-01\n", + " 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603\t-3.40625000e+00\t\t6.5974e-05\t8.3997e-07\t1.0000e+00\n", + " 604\t-3.40625000e+00\t\t6.4668e-05\t8.3997e-07\t1.0000e+00\n", + " 605\t-3.40625000e+00\t\t6.3387e-05\t8.3996e-07\t1.0000e+00\n", + " 606\t-3.40625000e+00\t\t6.2132e-05\t8.3996e-07\t1.0000e+00\n", + " 607\t-3.40625000e+00\t\t6.0902e-05\t8.3996e-07\t1.0000e+00\n", + " 608\t-3.40625000e+00\t\t5.9696e-05\t8.3996e-07\t1.0000e+00\n", + " 609\t-3.40625000e+00\t\t5.8514e-05\t8.3996e-07\t1.0000e+00\n", + " 610\t-3.40625000e+00\t\t5.7355e-05\t8.3996e-07\t1.0000e+00\n", + " 611\t-3.40625000e+00\t\t5.6219e-05\t8.3996e-07\t1.0000e+00\n", + " 612\t-3.40625000e+00\t\t5.5106e-05\t8.3996e-07\t1.0000e+00\n", + " 613\t-3.40625000e+00\t\t5.4015e-05\t8.3996e-07\t1.0000e+00\n", + " 614\t-3.40625000e+00\t\t5.2945e-05\t8.3996e-07\t1.0000e+00\n", + " 615\t-3.40625000e+00\t\t5.1897e-05\t8.3995e-07\t1.0000e+00\n", + " 616\t-3.40625000e+00\t\t5.0869e-05\t8.3995e-07\t1.0000e+00\n", + " 617\t-3.40625000e+00\t\t4.9862e-05\t8.3995e-07\t1.0000e+00\n", 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632\t-3.40625000e+00\t\t3.6938e-05\t8.3994e-07\t1.0000e+00\n", + " 633\t-3.40625000e+00\t\t3.6207e-05\t8.3994e-07\t1.0000e+00\n", + " 634\t-3.40625000e+00\t\t3.5490e-05\t8.3994e-07\t1.0000e+00\n", + " 635\t-3.40625000e+00\t\t3.4787e-05\t8.3994e-07\t1.0000e+00\n", + " 636\t-3.40625000e+00\t\t3.4098e-05\t8.3994e-07\t1.0000e+00\n", + " 637\t-3.40625000e+00\t\t3.3423e-05\t8.3994e-07\t1.0000e+00\n", + " 638\t-3.40625000e+00\t\t3.2761e-05\t8.3994e-07\t1.0000e+00\n", + " 639\t-3.40625000e+00\t\t3.2112e-05\t8.3994e-07\t1.0000e+00\n", + " 640\t-3.40625000e+00\t\t3.1477e-05\t8.3994e-07\t1.0000e+00\n", + " 641\t-3.40625000e+00\t\t3.0853e-05\t8.3994e-07\t1.0000e+00\n", + " 642\t-3.40625000e+00\t\t3.0242e-05\t8.3994e-07\t1.0000e+00\n", + " 643\t-3.40625000e+00\t\t2.9643e-05\t8.3994e-07\t1.0000e+00\n", + " 644\t-3.40625000e+00\t\t2.9056e-05\t8.3994e-07\t1.0000e+00\n", + " 645\t-3.40625000e+00\t\t2.8481e-05\t8.3994e-07\t1.0000e+00\n", + " 646\t-3.40625000e+00\t\t2.7917e-05\t8.3994e-07\t1.0000e+00\n", 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806\t-3.40625000e+00\t\t1.1378e-06\t8.3993e-07\t1.0000e+00\n", + " 807\t-3.40625000e+00\t\t1.1153e-06\t8.3993e-07\t1.0000e+00\n", + " 808\t-3.40625000e+00\t\t1.0932e-06\t8.3993e-07\t1.0000e+00\n", + " 809\t-3.40625000e+00\t\t1.0716e-06\t8.3993e-07\t1.0000e+00\n", + " 810\t-3.40625000e+00\t\t1.0504e-06\t8.3993e-07\t1.0000e+00\n", + " 811\t-3.40625000e+00\t\t1.0296e-06\t8.3993e-07\t1.0000e+00\n", + " 812\t-3.40625000e+00\t\t1.0092e-06\t8.3993e-07\t1.0000e+00\n", + " 813\t-3.40625000e+00\t\t9.8918e-07\t8.3993e-07\t1.0000e+00" + ] + }, + { + "data": { + "image/png": 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{}, + "output_type": "execute_result" + } + ], + "source": [ + "gmq(Q, q, fStar=v, alpha=2/(200+2), Plotf=1, printing=true) # 16 iterations instead of 12 but useful for higher dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "881f1f83-bde5-4ac3-81ec-57fd852e3b0e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10-27/lesson.ipynb b/10-27/lesson.ipynb new file mode 100644 index 0000000..2fd43ed --- /dev/null +++ b/10-27/lesson.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "40e2ecf6-a1ee-4d82-924a-e2f763915652", + "metadata": {}, + "outputs": [], + "source": [ + "using LinearAlgebra, Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "89746093-dc10-4bb2-9646-c84d5db0d8f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "householder_vector (generic function with 2 methods)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function householder_vector(x::Vector{<:AbstractFloat})::Tuple{Vector, AbstractFloat}\n", + " # returns the normalized vector u such that H*x is a multiple of e_1\n", + "\n", + " s = norm(x)\n", + " if x[1] ≥ 0\n", + " s = -s\n", + " end\n", + " u = copy(x)\n", + " u[1] -= s\n", + " u ./= norm(u)\n", + " return u, s\n", + "end\n", + "\n", + "function householder_vector(x::Matrix{<:AbstractFloat})::Tuple{Matrix, AbstractFloat}\n", + " # returns the normalized vector u such that H*x is a multiple of e_1\n", + "\n", + " s = norm(x)\n", + " if x[1] ≥ 0\n", + " s = -s\n", + " end\n", + " u = copy(x)\n", + " u[1] -= s\n", + " u ./= norm(u)\n", + " return u, s\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "262a769a-aa42-4929-bbf4-7f5a97783810", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([0.7960091839647405, 0.3357514552548967, 0.503627182882345], -3.7416573867739413)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3-element Vector{Float64}:\n", + " 1.0\n", + " 2.0\n", + " 3.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = [1., 2, 3]\n", + "householder_vector(x) |> display\n", + "x |> display\n", + "\n", + "# better with copy and division in place\n", + "# @benchmark householder_vector(randn(100_000))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "457b3bcf-a077-42cb-9a6c-f6d1d6e00504", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5-element Vector{Float64}:\n", + " 3.983324158315414\n", + " -5.457417586244235e-16\n", + " -4.598436127585974e-17\n", + " -1.4573782140540267e-16\n", + " 3.2867357162787514e-16" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A = randn(5, 4)\n", + "\n", + "# first step of QR factorization\n", + "R1 = A\n", + "(u1, s1) = householder_vector(R1[1:end,1])\n", + "\n", + "H1 = I - 2 * u1 * u1'\n", + "\n", + "Q1 = H1\n", + "\n", + "Q1 * R1[1:end, 1] |> display # what we expect -> a multiple of e_1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fd98a89e-01b9-4403-8bca-b9e1443b2eea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5×4 Matrix{Float64}:\n", + " 3.98332 -1.23134 1.8182 0.590776\n", + " 3.80466e-16 1.52712 -1.85383 -0.709258\n", + " -3.09958e-16 -1.11852e-17 -0.563749 -1.85235\n", + " 4.05338e-16 -4.61156e-17 -0.174643 1.27511\n", + " 1.55202e-16 1.4856e-17 1.73802 0.266999" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# second step\n", + "R2 = Q1 * R1\n", + "\n", + "(u2, s2) = householder_vector(R2[2:end, 2])\n", + "H2 = I - 2 * u2 * u2'\n", + "\n", + "# there is no blkdiag method in julia\n", + "# (maybe look into https://github.com/JuliaArrays/BlockDiagonals.jl)\n", + "# there are 2 methods (blocks is an array of blocks):\n", + "### METHOD 1:\n", + "# cat(blocks..., dims=(1,2))\n", + "### METHOD 2:\n", + "# using SparseArrays\n", + "# blockdiag(SparseMatrixCSC.(blocks)...)\n", + "## method 2 is slightly faster with subsequent matrix multiplication\n", + "# performance is ignored in this step\n", + "Q2 = cat(1, H2, dims=(1, 2))\n", + "\n", + "Q2 * R2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a563f47e-7eda-46c7-9804-080335bcb8a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5×4 Matrix{Float64}:\n", + " 3.98332 -1.23134 1.8182 0.590776\n", + " 3.80466e-16 1.52712 -1.85383 -0.709258\n", + " 2.03593e-16 2.18903e-17 1.83549 0.700423\n", + " 4.4272e-16 -4.37079e-17 6.42625e-17 1.46093\n", + " -2.16817e-16 -9.104e-18 -4.23002e-16 -1.58224" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# third step\n", + "\n", + "R3 = Q2 * R2\n", + "\n", + "(u3, s3) = householder_vector(R3[3:end, 3])\n", + "H3 = I - 2 * u3 * u3'\n", + "\n", + "Q3 = cat(Diagonal(ones(2)), H3, dims=(1,2))\n", + "Q3 * R3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "85f5eb54-f3fe-40c8-86d0-90e80895b753", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5×4 Matrix{Float64}:\n", + " 3.98332 -1.23134 1.8182 0.590776\n", + " 3.80466e-16 1.52712 -1.85383 -0.709258\n", + " 2.03593e-16 2.18903e-17 1.83549 0.700423\n", + " -4.5963e-16 2.29618e-17 -3.54379e-16 -2.15356\n", + " 1.78186e-16 -3.82887e-17 -2.39742e-16 1.50034e-16" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fourth step\n", + "\n", + "R4 = Q3 * R3\n", + "\n", + "(u4, s4) = householder_vector(R4[4:end, 4])\n", + "H4 = I - 2 * u4 * u4'\n", + "\n", + "Q4 = cat(Diagonal(ones(3)), H4, dims=(1,2))\n", + "Q4 * R4\n", + "\n", + "# done because we arrived at the second dimension of A" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "eff90e91-7856-4fd7-b2a5-79c0f681147a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "qrfactorization (generic function with 1 method)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function qrfactorization(A::Matrix{<:AbstractFloat})::Tuple{Matrix{<:AbstractFloat}, Matrix{<:AbstractFloat}}\n", + " (m, n) = size(A)\n", + " R = copy(A)\n", + " Q = Diagonal(ones(eltype(A), m))\n", + "\n", + " for k ∈ 1:n\n", + " (u, s) = householder_vector(R[k:end, k])\n", + " # construct R\n", + " R[k, k] = s\n", + " R[k+1:end, k] .= 0\n", + " R[k:end, k+1:end] -= 2 * u * (u' * R[k:end, k+1:end])\n", + " # contruct the new H\n", + " H = I - 2 * u * u'\n", + " # contruct the Q\n", + " Q = Q * cat(Diagonal(ones(eltype(A), k-1)), H, dims=(1,2)) # very inefficient (maybe simply send back the list of u_i)\n", + " end\n", + " return (Q, R)\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4dbe13ff-44f5-4bd2-8452-a9e1477c80ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "true" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A = randn(Float32, 1000, 20)\n", + "(Q, R) = qrfactorization(A)\n", + "(norm(A - Q*R) ≤ size(A)[1] * 2^-23 * norm(A)) |> display\n", + "(norm(I - Q*Q') ≤ size(A)[1] * 2^-23) |> display" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}