first commit with current lessons
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elvis
290
10-12/UNM.m
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290
10-12/UNM.m
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function [ x , status ] = UNM( f , varargin )
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%function [ x , status ] = UNM( f , x , eps , finf , MaxFeval )
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%
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% Apply the pure, non-globalised Newton Method for the minimization (or
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% maximization, since the method does not distinguish between the two) of
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% the provided one-dimensional (Univariate) function f, which must have
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% the following interface:
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%
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% [ v , varargout ] = f( x )
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%
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% Input:
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%
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% - x is either a scalar real denoting the input of f(), or [] (empty).
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%
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% Output:
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%
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% - v (real, scalar): if x == [] this is the best known lower bound on
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% the global optimum of f() on the standard interval in which f() is
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% supposed to be minimised (see next). If x ~= [] then v = f(x).
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%
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% - g (real, either scalar or a [ 2 x 1 ] matrix denoting an interval) is
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% the first optional argument. This also depends on x. if x == [] then
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% g is a [ 2 x 1 ] matrix denoting the standard interval in which f()
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% is supposed to be minimised (into which v is the minimum). f() is
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% never called with x ~= [].
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%
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% - H (real, scalar) is the second optional argument. This must only be
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% specified if x ~= [], and it is the second derivative h = f''(x).
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% If no such information is available, the function throws error.
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%
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% IMPORTANT NOTE: the function requires f() to be able to provide both the
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% first and the second derivative.
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%
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% The other [optional] input parameters:
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%
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% - x: (either a real scalar or [], default []): the starting point; if
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% x == [], the left extreme of default range point provided by f() is
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% used.
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%
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% - eps (real scalar, default value 1e-6): the accuracy in the stopping
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% criterion: the algorithm is stopped when a point is found such that
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% the absolute value of the derivative is less than or equal to eps.
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%
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% - finf (real scalar, default value 1e+8): since the non-globalised
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% Newton Method may diverge, a very rough divergence test is
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% implemented whereby if | f( x ) | >= finf then the algorithm is
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% stopped with an error condition.
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%
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% - MaxFeval (integer scalar, default value 30): the maximum number of
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% function evaluations (hence, iterations will be not more than
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% MaxFeval - 2 because at each iteration one function evaluation is
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% performed, except in the first one when two are).
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%
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% Output:
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%
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% - x (real scalar): the best solution found so far.
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%
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% - status (string): a string describing the status of the algorithm at
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% termination
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%
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% = 'optimal': the algorithm terminated having proven that x is a(n
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% approximately) optimal solution, i.e., the diameter of the
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% restricted range is less than or equal to delta.
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%
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% = 'stopped': the algorithm terminated having exhausted the maximum
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% number of iterations: x is the best solution found so far, but not
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% necessarily the optimal one
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%
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% = 'error': the algorithm found a numerical error that prevents it from
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% continuing optimization, such as finding f''( x ) very close to 0,
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% or it is found to be diverging (see finf above).
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%
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%{
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=======================================
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Author: Antonio Frangioni
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Date: 29-09-21
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Version 0.20
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Copyright Antonio Frangioni
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=======================================
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%}
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Plotg = 3;
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% 1 = the function value / gap are plotted
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% 2 = the function and the second-order model are plotted
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% 3 = the function, the first-order model and the second-order model are
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% plotted (the first-order model has no role in the algorithm, but
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% this shows how much better the second-order model is than the
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% first-order one)
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% all the rest: nothing is plotted
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Interactive = true; % if we pause at every iteration
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% reading and checking input- - - - - - - - - - - - - - - - - - - - - - - -
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% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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if ~ isa( f , 'function_handle' )
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error( 'f not a function' );
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end
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[ fStar , range ] = f( [] );
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if isempty( varargin ) || isempty( varargin{ 1 } )
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x = range( 1 );
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else
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x = varargin{ 1 };
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if ~ isreal( x ) || ~ isscalar( x )
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error( 'x not a real scakar' );
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end
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end
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if length( varargin ) > 1
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eps = varargin{ 2 };
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if ~ isreal( eps ) || ~ isscalar( eps )
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error( 'eps is not a real scalar' );
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end
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else
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eps = 1e-6;
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end
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if length( varargin ) > 2
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finf = varargin{ 3 };
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if ~ isreal( finf ) || ~ isscalar( finf )
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error( 'finf is not a real scalar' );
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end
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if finf <= 0
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error( 'finf must be in > 0' );
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end
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else
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finf = 1e+8;
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end
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if length( varargin ) > 3
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MaxFeval = round( varargin{ 4 } );
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if ~ isscalar( MaxFeval )
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error( 'MaxFeval is not an integer scalar' );
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end
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else
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MaxFeval = 30;
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end
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% initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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feval = 0;
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status = 'optimal';
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fbest = Inf;
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if Plotg == 1
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gap = [];
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end
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fprintf( 'Univariate Newton''s Method\n');
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if fStar > - Inf
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fprintf( 'feval\trel gap\t\tx\t\tf(x)\t\tf''(x)\t\tf''''(x)\n');
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else
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fprintf( 'feval\tfbest\t\tx\t\tf''(x)\t\tf''(x)\t\tf''''(x)\n');
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end
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% main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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while true
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% compute f( x ), f'( x ), f''( x ) - - - - - - - - - - - - - - - - - -
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[ fx , f1x , f2x ] = f( x );
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feval = feval + 1;
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if fx < fbest
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fbest = fx;
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end
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if abs( f2x ) <= 1e-16
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status = 'stopped';
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fprintf( 'numerical issue: f''''(x) = %1.4e\n' , f2x );
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break;
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end
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% main logic- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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xn = x - f1x / f2x;
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[ fx , f1x ] = f( x ); % compute f( x ) and f'( x )
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% output statistics - - - - - - - - - - - - - - - - - - - - - - - - - -
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if fStar > - Inf
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gapk = ( fbest - fStar ) / max( [ abs( fStar ) 1 ] );
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if Plotg == 1
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gap( end + 1 ) = gapk;
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semilogy( gap , 'Color' , 'k' , 'LineWidth' , 2 );
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xlim( [ 0 35 ] );
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ylim( [ 1e-15 inf ] );
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ax = gca;
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ax.FontSize = 16;
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ax.Position = [ 0.03 0.07 0.95 0.92 ];
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ax.Toolbar.Visible = 'off';
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end
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else
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gapk = fbest;
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end
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fprintf( '%4d\t%1.4e\t%1.8e\t%1.4e\t%1.4e\t%1.4e\n' , feval , ...
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gapk , x , fx , f1x , f2x );
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if Plotg > 1
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xm = min( [ x xn ] ) - abs( x - xn ) / 5;
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xp = max( [ x xn ] ) + abs( x - xn ) / 5;
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warning( 'off' , 'all' );
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fplot( @(x) f( x ) , [ xm xp ] , 'Color' , 'k' , 'LineWidth' , 1 );
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xlim( [ xm xp ] );
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yticks( [] );
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ax = gca;
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ax.FontSize = 16;
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ax.Toolbar.Visible = 'off';
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hold on;
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if Plotg == 3
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% first-order model is
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% f( y ) = f( x ) + f'( x )( y - x )
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% = [ f( x ) - f'( x ) x ]
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% + f'( x ) y
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b = f1x;
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c = fx - f1x * x;
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fplot( @(x) b * x + c , [ xm xp ] , ...
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'Color' , 'r' , 'LineWidth' , 1 );
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end
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% second-order model is
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% f( y ) = f( x ) + f'( x )( y - x ) + f''( x )( y - x )^2 / 2
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% = [ f( x ) - f'( x ) x + f''( x ) x^2 / 2 ]
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% + [ f'( x ) - f''( x ) x ] y
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% + f''( x )y^2 / 2
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a = f2x / 2;
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b = f1x - f2x * x;
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c = fx - f1x * x + f2x * x^2 / 2;
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fplot( @(x) a * x^2 + b * x + c , [ xm xp ] , ...
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'Color' , 'b' , 'LineWidth' , 1 );
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if x < xn
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xticks( [ xm x xn xp ] );
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xticklabels( { num2str( xm , '%1.1g' ) , 'x^k' , ...
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'x^{k+1}' , num2str( xp , '%1.1g' ) } );
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else
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xticks( [ xm xn x xp ] );
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xticklabels( { num2str( xm , '%1.1g' ) , 'x^{k+1}' , ...
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'x^k' , num2str( xp , '%1.1g' ) } );
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end
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warning( 'on' , 'all' );
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hold off;
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end
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% stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - -
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if abs( f1x ) <= eps
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break;
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end
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if feval > MaxFeval
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status = 'stopped';
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break;
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end
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if abs( fx ) > finf
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status = 'error';
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break;
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end
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if Interactive
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pause;
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end
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% iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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x = xn;
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end
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% end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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end % the end- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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