675 lines
24 KiB
Plaintext
Generated
675 lines
24 KiB
Plaintext
Generated
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"include(\"../QR/housQR.jl\")\n",
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"include(\"../utilities/genFunc.jl\")\n",
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"include(\"../L-BFGS/OracleFunction.jl\")\n",
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"include(\"../L-BFGS/LBFGS.jl\")\n",
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"include(\"../L-BFGS/BFGS.jl\")\n",
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"include(\"../L-BFGS/SR1.jl\")\n",
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"using .LBFGS\n",
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"using .BFGS\n",
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"using .SR1\n",
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"using .OracleFunction\n",
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"using .housQR\n",
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"using LinearAlgebra, BenchmarkTools, CSV, DataFrames\n",
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"\n",
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"baseDir = \"results/comparison\";\n",
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"mkpath(baseDir);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# QR vs LBFGS running time comparison with respect to n, fixing m, on ill-conditioned matrix\n",
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"λ = 1e-12\n",
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"m = 200\n",
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"runs = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"QRvsLBFGS-n-m\" * string(m) * \"-illcond--time.csv\");\n",
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"accData = Dict(\n",
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" :n => Array{Float64}(undef, 0),\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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" :meantimeQR => Array{Float64}(undef, 0),\n",
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" :stdtimeQR => Array{Float64}(undef, 0),\n",
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" :meanallocsQR => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"for n ∈ (0:runs) .* 500 .+ 500\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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"\n",
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" t_qr = @benchmark qrfact($gf[:X_hat]) \\ $gf[:y_hat] samples=10 evals=1\n",
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"\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t_lbfgs = @benchmark LimitedMemoryBFGS($ls) samples=10 evals=1\n",
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" \n",
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" push!(accData[:n], n)\n",
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" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
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" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
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" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
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" push!(accData[:meantimeQR], mean(t_qr.times))\n",
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" push!(accData[:stdtimeQR], std(t_qr.times))\n",
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" push!(accData[:meanallocsQR], mean(t_qr.memory))\n",
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" println(\"Done: n \" * string(n))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"# CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# QR vs LBFGS running time comparison with respect to n, fixing m, on well-conditioned matrix\n",
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"λ = 1e-4\n",
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"m = 200\n",
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"runs = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"QRvsLBFGS-n-m\" * string(m) * \"-wellcond--time.csv\");\n",
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"accData = Dict(\n",
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" :n => Array{Float64}(undef, 0),\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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" :meantimeQR => Array{Float64}(undef, 0),\n",
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" :stdtimeQR => Array{Float64}(undef, 0),\n",
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" :meanallocsQR => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"for n ∈ (0:runs) .* 500 .+ 500\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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"\n",
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" t_qr = @benchmark qrfact($gf[:X_hat]) \\ $gf[:y_hat] samples=10 evals=1\n",
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"\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t_lbfgs = @benchmark LimitedMemoryBFGS($ls) samples=10 evals=1\n",
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" \n",
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" push!(accData[:n], n)\n",
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" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
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" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
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" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
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" push!(accData[:meantimeQR], mean(t_qr.times))\n",
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" push!(accData[:stdtimeQR], std(t_qr.times))\n",
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" push!(accData[:meanallocsQR], mean(t_qr.memory))\n",
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" println(\"Done: n \" * string(n))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"# CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done: n 500\n",
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"Done: n 1000\n",
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"Done: n 1500\n",
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"Done: n 2000\n",
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"Done: n 2500\n",
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"Done: n 3000\n",
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"Done: n 3500\n",
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"Done: n 4000\n",
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"Done: n 4500\n",
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"Done: n 5000\n",
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"Done: n 5500\n"
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]
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}
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],
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"source": [
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"# BFGS vs LBFGS running time comparison with respect to n, fixing m, on well-conditioned matrix\n",
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"λ = 1e-4\n",
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"m = 200\n",
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"runs = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"BFGSvsLBFGS-n-m\" * string(m) * \"--time.csv\");\n",
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"accData = Dict(\n",
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" :n => Array{Float64}(undef, 0),\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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" :meantimeBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsBFGS => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"for i ∈ 0:runs\n",
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" n = 500 + i * 500\n",
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"\n",
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"\n",
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" t_lbfgs = @benchmark LimitedMemoryBFGS(ls) setup=(ls = (genFunc(:randDataset, λ=$λ, m=$m, n=$n) |> LeastSquaresF)) samples=10 evals=1\n",
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" t_bfgs = @benchmark BroydenFletcherGoldfarbShanno(ls) setup=(ls = (genFunc(:randDataset, λ=$λ, m=$m, n=$n) |> LeastSquaresF)) samples=10 evals=1\n",
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"\n",
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" push!(accData[:n], n)\n",
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" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
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" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
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" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
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" push!(accData[:meantimeBFGS], mean(t_bfgs.times))\n",
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" push!(accData[:stdtimeBFGS], std(t_bfgs.times))\n",
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" push!(accData[:meanallocsBFGS], mean(t_bfgs.memory))\n",
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" println(\"Done: n \" * string(n))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done: m 500\n",
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"Done: m 1000\n",
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"Done: m 1500\n",
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"Done: m 2000\n",
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"Done: m 2500\n",
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"Done: m 3000\n",
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"Done: m 3500\n",
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"Done: m 4000\n",
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"Done: m 4500\n",
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"Done: m 5000\n",
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"Done: m 5500\n"
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]
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}
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],
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"source": [
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"# BFGS vs LBFGS running time comparison with respect to m, fixing n, on well-conditioned matrix\n",
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"λ = 1e-4\n",
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"n = 50\n",
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"runs = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"BFGSvsLBFGS-m-n\" * string(n) * \"--time.csv\");\n",
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"accData = Dict(\n",
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" :m => Array{Float64}(undef, 0),\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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" :meantimeBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsBFGS => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"for i ∈ 0:runs\n",
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" m = 500 + i * 500\n",
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"\n",
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"\n",
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" t_lbfgs = @benchmark LimitedMemoryBFGS(ls) setup=(ls = (genFunc(:randDataset, λ=$λ, m=$m, n=$n) |> LeastSquaresF)) samples=10 evals=1\n",
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" t_bfgs = @benchmark BroydenFletcherGoldfarbShanno(ls) setup=(ls = (genFunc(:randDataset, λ=$λ, m=$m, n=$n) |> LeastSquaresF)) samples=10 evals=1\n",
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"\n",
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" push!(accData[:m], m)\n",
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" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
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" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
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" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
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" push!(accData[:meantimeBFGS], mean(t_bfgs.times))\n",
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" push!(accData[:stdtimeBFGS], std(t_bfgs.times))\n",
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" push!(accData[:meanallocsBFGS], mean(t_bfgs.memory))\n",
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" println(\"Done: m \" * string(m))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# QR vs LBFGS running time comparison with respect to m, fixing n, on well-conditioned matrix\n",
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"λ = 1e-4\n",
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"n = 50\n",
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"runs = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"QRvsLBFGS-m-n\" * string(n) * \"-wellcond--time.csv\");\n",
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"accData = Dict(\n",
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" :m => Array{Float64}(undef, 0),\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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" :meantimeQR => Array{Float64}(undef, 0),\n",
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" :stdtimeQR => Array{Float64}(undef, 0),\n",
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" :meanallocsQR => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"# warm up\n",
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"qrfact([0. 1; 1 0]) \\ [1.; 1]\n",
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"\n",
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"for i ∈ 0:runs\n",
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" m = 500 + i * 500\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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"\n",
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" t_qr = @benchmark begin\n",
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" QR = qrfact($gf[:X_hat])\n",
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" w = QR \\ $gf[:y_hat]\n",
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" end samples=10 evals=1\n",
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"\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t_lbfgs = @benchmark begin\n",
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" LimitedMemoryBFGS($ls)\n",
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" end samples=10 evals=1\n",
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" \n",
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" push!(accData[:m], m)\n",
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" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
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" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
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" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
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" push!(accData[:meantimeQR], mean(t_qr.times))\n",
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" push!(accData[:stdtimeQR], std(t_qr.times))\n",
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" push!(accData[:meanallocsQR], mean(t_qr.memory))\n",
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" println(\"Done: m \" * string(m))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# QR vs LBFGS running time comparison with respect to m, fixing n, on ill-conditioned matrix\n",
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"λ = 1e-12\n",
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"n = 50\n",
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"runs = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"QRvsLBFGS-m-n\" * string(n) * \"-illcond--time.csv\");\n",
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"accData = Dict(\n",
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" :m => Array{Float64}(undef, 0),\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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" :meantimeQR => Array{Float64}(undef, 0),\n",
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" :stdtimeQR => Array{Float64}(undef, 0),\n",
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" :meanallocsQR => Array{Float64}(undef, 0)\n",
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" )\n",
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"\n",
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"# warm up\n",
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"qrfact([0. 1; 1 0]) \\ [1.; 1]\n",
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"\n",
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"for i ∈ 0:runs\n",
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" m = 500 + i * 500\n",
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" gf = genFunc(:exactRandDataset, λ=λ, m=m, n=n)\n",
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"\n",
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" t_qr = @benchmark begin\n",
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" QR = qrfact($gf[:X_hat])\n",
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" w = QR \\ $gf[:y_hat]\n",
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" end samples=10 evals=1\n",
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"\n",
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" ls = LeastSquaresF(gf)\n",
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"\n",
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" t_lbfgs = @benchmark begin\n",
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" LimitedMemoryBFGS($ls)\n",
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" end samples=10 evals=1\n",
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" \n",
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" push!(accData[:m], m)\n",
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" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
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" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
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" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
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" push!(accData[:meantimeQR], mean(t_qr.times))\n",
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" push!(accData[:stdtimeQR], std(t_qr.times))\n",
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" push!(accData[:meanallocsQR], mean(t_qr.memory))\n",
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" println(\"Done: m \" * string(m))\n",
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" flush(stdout)\n",
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"end\n",
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"\n",
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"CSV.write(outputvsc, DataFrame(accData));"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Comparison of all quasi newton methods"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done: m 500\n",
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"Done: m 1000\n",
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"Done: m 1500\n",
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"Done: m 2000\n",
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"Done: m 2500\n",
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"Done: m 3000\n",
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"Done: m 3500\n",
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"Done: m 4000\n",
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"Done: m 4500\n",
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"Done: m 5000\n",
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"Done: m 5500\n"
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]
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}
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],
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"source": [
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"# Quasi newton methods running time comparison, fixing n and varying m, on ill-conditioned matrix\n",
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"λ = 1e-12\n",
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"n = 50\n",
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"runs = 10\n",
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"samples = 10\n",
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"\n",
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"# output csv\n",
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"outputvsc = joinpath(baseDir, \"Quasi-Newton-Comparison-time-illcond.csv\");\n",
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"accData = Dict(\n",
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" :m => Array{Float64}(undef, 0),\n",
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"\n",
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" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
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" :accuracyLBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
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"\n",
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" :meantimeBFGS => Array{Float64}(undef, 0),\n",
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" :stdtimeBFGS => Array{Float64}(undef, 0),\n",
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" :accuracyBFGS => Array{Float64}(undef, 0),\n",
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" :meanallocsBFGS => Array{Float64}(undef, 0),\n",
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"\n",
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" :meantimeBFGSDogleg => Array{Float64}(undef, 0),\n",
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" :stdtimeBFGSDogleg => Array{Float64}(undef, 0),\n",
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" :accuracyBFGSDogleg => Array{Float64}(undef, 0),\n",
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" :meanallocsBFGSDogleg => Array{Float64}(undef, 0),\n",
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"\n",
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" :meantimeDFP => Array{Float64}(undef, 0),\n",
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" :stdtimeDFP => Array{Float64}(undef, 0),\n",
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" :accuracyDFP => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsDFP => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
" :accuracyDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeSR1 => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeSR1 => Array{Float64}(undef, 0),\n",
|
|
" :accuracySR1 => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsSR1 => Array{Float64}(undef, 0)\n",
|
|
" \n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
"for i ∈ 0:runs\n",
|
|
" m = 500 + i * 500\n",
|
|
"\n",
|
|
" gf = genFunc(:randDataset, λ=λ, m=m, n=n)\n",
|
|
" ls = LeastSquaresF(gf)\n",
|
|
"\n",
|
|
" t_lbfgs = @benchmark begin\n",
|
|
" LimitedMemoryBFGS($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_lbfgs = LimitedMemoryBFGS(ls)\n",
|
|
"\n",
|
|
"\n",
|
|
" BFGS.BFGSorDFP = :BFGS\n",
|
|
"\n",
|
|
" t_bfgs = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShanno($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_bfgs = BroydenFletcherGoldfarbShanno(ls)\n",
|
|
"\n",
|
|
" t_bfgs_dogleg = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShannoDogleg($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_bfgs_dogleg = BroydenFletcherGoldfarbShannoDogleg(ls)\n",
|
|
"\n",
|
|
" BFGS.BFGSorDFP = :DFP\n",
|
|
"\n",
|
|
" t_dfp = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShanno($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_dfp = BroydenFletcherGoldfarbShanno(ls)\n",
|
|
"\n",
|
|
" t_dfp_dogleg = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShannoDogleg($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_dfp_dogleg = BroydenFletcherGoldfarbShannoDogleg(ls)\n",
|
|
"\n",
|
|
" t_sr1 = @benchmark begin\n",
|
|
" SymmetricRank1($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_sr1 = SymmetricRank1(ls)\n",
|
|
" \n",
|
|
" push!(accData[:m], m)\n",
|
|
" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
|
|
" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
|
|
" push!(accData[:accuracyLBFGS], norm(res_lbfgs.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeBFGS], mean(t_bfgs.times))\n",
|
|
" push!(accData[:stdtimeBFGS], std(t_bfgs.times))\n",
|
|
" push!(accData[:accuracyBFGS], norm(res_bfgs.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsBFGS], mean(t_bfgs.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeBFGSDogleg], mean(t_bfgs_dogleg.times))\n",
|
|
" push!(accData[:stdtimeBFGSDogleg], std(t_bfgs_dogleg.times))\n",
|
|
" push!(accData[:accuracyBFGSDogleg], norm(res_bfgs_dogleg.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsBFGSDogleg], mean(t_bfgs_dogleg.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeDFP], mean(t_dfp.times))\n",
|
|
" push!(accData[:stdtimeDFP], std(t_dfp.times))\n",
|
|
" push!(accData[:accuracyDFP], norm(res_dfp.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsDFP], mean(t_dfp.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeDFPDogleg], mean(t_dfp_dogleg.times))\n",
|
|
" push!(accData[:stdtimeDFPDogleg], std(t_dfp_dogleg.times))\n",
|
|
" push!(accData[:accuracyDFPDogleg], norm(res_dfp_dogleg.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsDFPDogleg], mean(t_dfp_dogleg.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeSR1], mean(t_sr1.times))\n",
|
|
" push!(accData[:stdtimeSR1], std(t_sr1.times))\n",
|
|
" push!(accData[:accuracySR1], norm(res_sr1.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsSR1], mean(t_sr1.memory))\n",
|
|
" println(\"Done: m \" * string(m))\n",
|
|
" flush(stdout)\n",
|
|
"end\n",
|
|
"\n",
|
|
"CSV.write(outputvsc, DataFrame(accData));"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Done: m 500\n",
|
|
"Done: m 1000\n",
|
|
"Done: m 1500\n",
|
|
"Done: m 2000\n",
|
|
"Done: m 2500\n",
|
|
"Done: m 3000\n",
|
|
"Done: m 3500\n",
|
|
"Done: m 4000\n",
|
|
"Done: m 4500\n",
|
|
"Done: m 5000\n",
|
|
"Done: m 5500\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Quasi newton methods running time comparison, fixing n and varying m, on well-conditioned matrix\n",
|
|
"λ = 1e-4\n",
|
|
"n = 50\n",
|
|
"runs = 10\n",
|
|
"samples = 10\n",
|
|
"\n",
|
|
"# output csv\n",
|
|
"outputvsc = joinpath(baseDir, \"Quasi-Newton-Comparison-time-wellcond.csv\");\n",
|
|
"accData = Dict(\n",
|
|
" :m => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeLBFGS => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeLBFGS => Array{Float64}(undef, 0),\n",
|
|
" :accuracyLBFGS => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsLBFGS => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeBFGS => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeBFGS => Array{Float64}(undef, 0),\n",
|
|
" :accuracyBFGS => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsBFGS => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeBFGSDogleg => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeBFGSDogleg => Array{Float64}(undef, 0),\n",
|
|
" :accuracyBFGSDogleg => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsBFGSDogleg => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeDFP => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeDFP => Array{Float64}(undef, 0),\n",
|
|
" :accuracyDFP => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsDFP => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
" :accuracyDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsDFPDogleg => Array{Float64}(undef, 0),\n",
|
|
"\n",
|
|
" :meantimeSR1 => Array{Float64}(undef, 0),\n",
|
|
" :stdtimeSR1 => Array{Float64}(undef, 0),\n",
|
|
" :accuracySR1 => Array{Float64}(undef, 0),\n",
|
|
" :meanallocsSR1 => Array{Float64}(undef, 0)\n",
|
|
" \n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
"for i ∈ 0:runs\n",
|
|
" m = 500 + i * 500\n",
|
|
"\n",
|
|
" gf = genFunc(:randDataset, λ=λ, m=m, n=n)\n",
|
|
" ls = LeastSquaresF(gf)\n",
|
|
"\n",
|
|
" t_lbfgs = @benchmark begin\n",
|
|
" LimitedMemoryBFGS($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_lbfgs = LimitedMemoryBFGS(ls)\n",
|
|
"\n",
|
|
"\n",
|
|
" BFGS.BFGSorDFP = :BFGS\n",
|
|
"\n",
|
|
" t_bfgs = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShanno($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_bfgs = BroydenFletcherGoldfarbShanno(ls)\n",
|
|
"\n",
|
|
" t_bfgs_dogleg = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShannoDogleg($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_bfgs_dogleg = BroydenFletcherGoldfarbShannoDogleg(ls)\n",
|
|
"\n",
|
|
" BFGS.BFGSorDFP = :DFP\n",
|
|
"\n",
|
|
" t_dfp = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShanno($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_dfp = BroydenFletcherGoldfarbShanno(ls)\n",
|
|
"\n",
|
|
" t_dfp_dogleg = @benchmark begin\n",
|
|
" BroydenFletcherGoldfarbShannoDogleg($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_dfp_dogleg = BroydenFletcherGoldfarbShannoDogleg(ls)\n",
|
|
"\n",
|
|
" t_sr1 = @benchmark begin\n",
|
|
" SymmetricRank1($ls)\n",
|
|
" end samples=samples evals=1\n",
|
|
" res_sr1 = SymmetricRank1(ls)\n",
|
|
" \n",
|
|
" push!(accData[:m], m)\n",
|
|
" push!(accData[:meantimeLBFGS], mean(t_lbfgs.times))\n",
|
|
" push!(accData[:stdtimeLBFGS], std(t_lbfgs.times))\n",
|
|
" push!(accData[:accuracyLBFGS], norm(res_lbfgs.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsLBFGS], mean(t_lbfgs.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeBFGS], mean(t_bfgs.times))\n",
|
|
" push!(accData[:stdtimeBFGS], std(t_bfgs.times))\n",
|
|
" push!(accData[:accuracyBFGS], norm(res_bfgs.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsBFGS], mean(t_bfgs.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeBFGSDogleg], mean(t_bfgs_dogleg.times))\n",
|
|
" push!(accData[:stdtimeBFGSDogleg], std(t_bfgs_dogleg.times))\n",
|
|
" push!(accData[:accuracyBFGSDogleg], norm(res_bfgs_dogleg.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsBFGSDogleg], mean(t_bfgs_dogleg.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeDFP], mean(t_dfp.times))\n",
|
|
" push!(accData[:stdtimeDFP], std(t_dfp.times))\n",
|
|
" push!(accData[:accuracyDFP], norm(res_dfp.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsDFP], mean(t_dfp.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeDFPDogleg], mean(t_dfp_dogleg.times))\n",
|
|
" push!(accData[:stdtimeDFPDogleg], std(t_dfp_dogleg.times))\n",
|
|
" push!(accData[:accuracyDFPDogleg], norm(res_dfp_dogleg.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsDFPDogleg], mean(t_dfp_dogleg.memory))\n",
|
|
"\n",
|
|
" push!(accData[:meantimeSR1], mean(t_sr1.times))\n",
|
|
" push!(accData[:stdtimeSR1], std(t_sr1.times))\n",
|
|
" push!(accData[:accuracySR1], norm(res_sr1.x - gf[:w_star]) / norm(gf[:w_star]))\n",
|
|
" push!(accData[:meanallocsSR1], mean(t_sr1.memory))\n",
|
|
" println(\"Done: m \" * string(m))\n",
|
|
" flush(stdout)\n",
|
|
"end\n",
|
|
"\n",
|
|
"CSV.write(outputvsc, DataFrame(accData));"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": 4
|
|
}
|