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runExperiments.py
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runExperiments.py
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if __name__== "__main__":
import os
#Computing parameters.
os.environ["OMP_NUM_THREADS"] = "4" # export OMP_NUM_THREADS=4
os.environ["OPENBLAS_NUM_THREADS"] = "4" # export OPENBLAS_NUM_THREADS=4
os.environ["MKL_NUM_THREADS"] = "6" # export MKL_NUM_THREADS=6
os.environ["VECLIB_MAXIMUM_THREADS"] = "4" # export VECLIB_MAXIMUM_THREADS=4
os.environ["NUMEXPR_NUM_THREADS"] = "6" # export NUMEXPR_NUM_THREADS=6
#General imports
import numpy as np
import os
import time
import datetime
import matplotlib.pyplot as plt
from scipy.sparse import csc_matrix
from algorithms import CGS, LaCG, runFW, DIPFW, catalystScheme
from functions import funcQuadraticDiag, funcAccelSchemeDiag, funcAccelScheme, funcQuadratic
from auxiliaryFunctions import randomPSDGeneratorSparse, calculateEigenvalues
"""
------------------------------BIRKHOFF POLYTOPE----------------------------
"""
from feasibleRegions import BirkhoffPolytope
#Time limit spent calculating the reference solution and running the algorithm.
TIME_LIMIT_REFERENCE_SOL = int(4*3600)
TIME_LIMIT = int(1800)
size = int(40*40)
feasibleRegion = BirkhoffPolytope(size)
x_0 = feasibleRegion.initialPoint()
S_0 = [x_0]
alpha_0 = [1]
tolerance = 1.0e-2
typeOfStep = "SS"
#Define the objective function.
sparsity = 0.01
M = randomPSDGeneratorSparse(size, sparsity)
L, Mu = calculateEigenvalues(M)
Matrix = csc_matrix(M)
LVal = L
MuVal = Mu
b = np.zeros(size)
fun = funcQuadratic(size, Matrix, b, Mu, L)
print("Solving the problem over the Birkhoff polytope.")
##Run to a high Frank-Wolfe primal gap accuracy for later use.
print("\nFinding optimal solution to high accuracy using DIPFW or Lazy AFW.")
xTest, FWGapTest, fValTest, timingTest, activeSetTest = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance/2.0, TIME_LIMIT_REFERENCE_SOL, FWVariant = "Lazy", typeStep = "EL", criterion = "DG")
fValOpt = fValTest[-1]
tolerance = min(np.asarray(FWGapTest))
#LaCG
print("\nRunning LaCG-AFW.")
LaCGAway = LaCG()
xLaCGAFW, FWGapLaCGAFW, fValLaCGAFW, timingLaCGAFW, activeLaCGAFW = LaCGAway.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCG = LaCGAway.returnRestarts()
restartsLaCG[:] = np.asarray([x - 1 for x in restartsLaCG])
#LaCG Lazy
print("\nRunning LaCG-AFW Lazy.")
LaCGAwayLazy = LaCG()
xLaCGAFWLazy, FWGapLaCGAFWLazy, fValLaCGAFWLazy, timingLaCGAFWLazy, activeLaCGAFWLazy = LaCGAwayLazy.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGLazy = LaCGAwayLazy.returnRestarts()
restartsLaCGLazy[:] = np.asarray([x - 1 for x in restartsLaCGLazy])
#LaCG PFW
print("\nRunning LaCG-PFW.")
LaCGPairwise = LaCG()
xLaCGPFW, FWGapLaCGPFW, fValLaCGPFW, timingLaCGPFW, activeLaCGPFW = LaCGPairwise.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGPFW = LaCGPairwise.returnRestarts()
restartsLaCGPFW[:] = np.asarray([x - 1 for x in restartsLaCGPFW])
#Vanilla PFW
print("\nRunning PFW.")
xPFW, FWGapPFW, fValPFW, timingPFW, activeSetAFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Vanilla AFW
print("\nRunning AFW.")
xAFW, FWGapAFW, fValAFW, timingAFW, activeSetPFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Run Lazy AFW
print("\nRunning Lazy AFW.")
xAFWLazy, FWGapAFWLazy, fValAFWLazy, timingAFWLazy, activeSetLazy = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Decomposition Invariant CG
print("\nRunning DICG.")
xDICG, FWGapDICG, fValDICG, timingDICG = DIPFW(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Catalyst augmented
print("\nRunning Catalyst-augmented AFW.")
funCat = funcAccelScheme(len(x_0), fun.returnM(), fun.returnb(), fun.largestEig(), fun.smallestEig())
CatalystAFW = catalystScheme()
xCatalyst, FWCatalyst, fCatalyst, tCatalyst, itCatalyst = CatalystAFW.run(x_0, funCat, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = "SS", criterionRef = fValOpt)
#Conditional Gradient Sliding.
print("\nRunning CGS.")
CGS = CGS()
xCGS, FWGapCGS, fValCGS, timingCGS, iterationCGS = CGS.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, criterion = "PG", criterionRef = fValOpt)
#Plot primal gap in terms of iteration.
plt.loglog(np.arange(len(fValAFW)), [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = np.logspace(0, np.log10(len(fValAFW)-1), 10).astype(int).tolist(), label = 'AFW')
plt.loglog(np.arange(len(fValPFW)), [(x - fValOpt) for x in fValPFW], '-D', color = 'c', markevery = np.logspace(0, np.log10(len(fValPFW)-1), 10).astype(int).tolist(), label = 'PFW')
plt.loglog(np.arange(len(fValAFWLazy)), [(x - fValOpt) for x in fValAFWLazy], '--*', color = 'b', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValAFWLazy)-1), 10).astype(int).tolist(), label = 'AFW (L)')
plt.loglog(np.arange(len(fValLaCGAFW)), [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = np.logspace(0, np.log10(len(fValLaCGAFW)-1), 10).astype(int).tolist(), label = 'LaCG-AFW')
plt.loglog(np.arange(len(fValLaCGPFW)), [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = np.logspace(0, np.log10(len(fValLaCGPFW)-1), 10).astype(int).tolist(), label = 'LaCG-PFW')
plt.loglog(np.arange(len(fValLaCGAFWLazy)), [(x - fValOpt) for x in fValLaCGAFWLazy], '--o', color = 'k', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValLaCGAFWLazy)-1), 10).astype(int).tolist(), label = 'LaCG-AFW (L)')
plt.loglog(np.arange(len(fValDICG)), [(x - fValOpt) for x in fValDICG], '-s', color = 'r', markevery = np.logspace(0, np.log10(len(fValDICG)-1), 10).astype(int).tolist(), label = 'DICG')
plt.loglog(iterationCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.loglog(itCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.xlabel(r'$k$')
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.grid()
plt.show()
plt.close()
#Plot Primal gap in terms of time.
plt.semilogy(timingAFW, [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = int(len(timingAFW)/10), label = 'AFW')
plt.semilogy(timingPFW, [(x - fValOpt) for x in fValPFW], '-D', color = 'g', markevery = int(len(timingPFW)/10), label = 'PFW')
plt.semilogy(timingLaCGAFW, [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = int(len(timingLaCGAFW)/10), label = 'LaCG-AFW')
plt.semilogy(timingLaCGPFW, [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = int(len(timingLaCGPFW)/10), label = 'LaCG-PFW')
plt.semilogy(timingDICG, [(x - fValOpt) for x in fValDICG], '-s', color = 'r', markevery = int(len(timingDICG)/10), label = 'DICG')
plt.semilogy(timingCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.semilogy(tCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.xlabel(r't[s]')
plt.grid()
plt.show()
plt.close()
# #Generate a timestamp for the example and save data
# ts = time.time()
# timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S').replace(' ', '-').replace(':', '-')
# with open(os.path.join(os.getcwd(), "Birkhoff_" + str(timestamp) + "_Mu" + str(MuVal) + "_L" + str(LVal)+ "_Size" + str(size) + "_TypeStep_" + typeOfStep + ".txt"), 'w') as f:
# f.write("size:\t" + str(len(x_0)) + "\n")
# f.write("Mu:\t" + str(fun.smallestEig())+ "\n")
# f.write("LVal:\t" + str(fun.largestEig())+ "\n")
# f.write("Tolerance:\t" + str(tolerance)+ "\n")
# f.write("Optimum:\t" + str(fValOpt)+ "\n")
# #Output the FW Gap.
# f.write("AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetAFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetPFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("Lazy AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetLazy).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCG) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW Lazy"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGLazy) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write("CGS"+ "\n")
# f.write(str([x - fValOpt for x in fValCGS]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingCGS) .replace("[", "").replace("]", "") + "\n")
# f.write(str(iterationCGS) .replace("[", "").replace("]", "") + "\n")
# f.write("DICG"+ "\n")
# f.write(str([x - fValOpt for x in fValDICG]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValDICG).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapDICG).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingDICG) .replace("[", "").replace("]", "") + "\n")
# f.write("Catalyst"+ "\n")
# f.write(str([x - fValOpt for x in fCatalyst]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(tCatalyst) .replace("[", "").replace("]", "") + "\n")
# f.write(str(itCatalyst) .replace("[", "").replace("]", "") + "\n")
"""
------------------------------MIPLIB POLYTOPE----------------------------
"""
from feasibleRegions import GurobiPolytope
from auxiliaryFunctions import randomPSDGenerator
#Good Instance
file = 'ran14x18-disj-8.mps'
TIME_LIMIT_REFERENCE_SOL = int(1800)
TIME_LIMIT = int(800)
MuVal = 0.1
LVal = 10.0
typeOfStep = "SS"
tolerance = 0.1
size = 504
#Create the objective function.
file = 'ran14x18-disj-8.mps'
matrix = randomPSDGenerator(size, MuVal, LVal)
b = np.zeros(size)
fun = funcQuadratic(size, matrix, b, MuVal, LVal)
pathfile = os.path.join(os.getcwd(), 'MIPLIB', file)
feasibleRegion = GurobiPolytope(pathfile)
size = feasibleRegion.dim()
#Initial point
x_0 = feasibleRegion.initialPoint()
S_0 = [x_0]
alpha_0 = [1]
##Run to a high Frank-Wolfe primal gap accuracy for later use.
print("Finding optimal solution to high accuracy using DIPFW or Lazy AFW.")
xTest, FWGapTest, fValTest, timingTest, activeSetTest = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance/2.0, TIME_LIMIT_REFERENCE_SOL, FWVariant = "Lazy", typeStep = "EL", criterion = "DG")
fValOpt = fValTest[-1]
tolerance = min(np.asarray(FWGapTest))
#Conditional Gradient Sliding.
print("Running CGS.")
CGS = CGS()
xCGS, FWGapCGS, fValCGS, timingCGS, iterationCGS = CGS.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, criterion = "PG", criterionRef = fValOpt)
#Catalyst augmented
print("Running Catalyst-augmented AFW.")
funCat = funcAccelScheme(len(x_0), fun.returnM(), fun.returnb(), fun.largestEig(), fun.smallestEig())
CatalystAFW = catalystScheme()
xCatalyst, FWCatalyst, fCatalyst, tCatalyst, itCatalyst = CatalystAFW.run(x_0, funCat, feasibleRegion, tolerance, TIME_LIMIT)
#LaCG
print("Running LaCG-AFW.")
LaCGAway = LaCG()
xLaCGAFW, FWGapLaCGAFW, fValLaCGAFW, timingLaCGAFW, activeLaCGAFW = LaCGAway.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCG = LaCGAway.returnRestarts()
restartsLaCG[:] = np.asarray([x - 1 for x in restartsLaCG])
#LaCG Lazy
print("Running LaCG-AFW Lazy.")
LaCGAwayLazy = LaCG()
xLaCGAFWLazy, FWGapLaCGAFWLazy, fValLaCGAFWLazy, timingLaCGAFWLazy, activeLaCGAFWLazy = LaCGAwayLazy.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGLazy = LaCGAwayLazy.returnRestarts()
restartsLaCGLazy[:] = np.asarray([x - 1 for x in restartsLaCGLazy])
#LaCG PFW
print("Running LaCG-PFW.")
LaCGPairwise = LaCG()
xLaCGPFW, FWGapLaCGPFW, fValLaCGPFW, timingLaCGPFW, activeLaCGPFW = LaCGPairwise.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGPFW = LaCGPairwise.returnRestarts()
restartsLaCGPFW[:] = np.asarray([x - 1 for x in restartsLaCGPFW])
#Vanilla PFW
print("Running PFW.")
xPFW, FWGapPFW, fValPFW, timingPFW, activeSetAFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Vanilla AFW
print("Running AFW.")
xAFW, FWGapAFW, fValAFW, timingAFW, activeSetPFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Run Lazy AFW
print("Running Lazy AFW.")
xAFWLazy, FWGapAFWLazy, fValAFWLazy, timingAFWLazy, activeSetLazy = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Plot primal gap in terms of iteration.
plt.loglog(np.arange(len(fValAFW)), [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = np.logspace(0, np.log10(len(fValAFW)-1), 10).astype(int).tolist(), label = 'AFW')
plt.loglog(np.arange(len(fValPFW)), [(x - fValOpt) for x in fValPFW], '-D', color = 'c', markevery = np.logspace(0, np.log10(len(fValPFW)-1), 10).astype(int).tolist(), label = 'PFW')
plt.loglog(np.arange(len(fValAFWLazy)), [(x - fValOpt) for x in fValAFWLazy], '--*', color = 'b', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValAFWLazy)-1), 10).astype(int).tolist(), label = 'AFW (L)')
plt.loglog(np.arange(len(fValLaCGAFW)), [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = np.logspace(0, np.log10(len(fValLaCGAFW)-1), 10).astype(int).tolist(), label = 'LaCG-AFW')
plt.loglog(np.arange(len(fValLaCGPFW)), [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = np.logspace(0, np.log10(len(fValLaCGPFW)-1), 10).astype(int).tolist(), label = 'LaCG-PFW')
plt.loglog(np.arange(len(fValLaCGAFWLazy)), [(x - fValOpt) for x in fValLaCGAFWLazy], '--o', color = 'k', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValLaCGAFWLazy)-1), 10).astype(int).tolist(), label = 'LaCG-AFW (L)')
plt.loglog(iterationCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.loglog(itCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.xlabel(r'$k$')
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.grid()
plt.show()
plt.close()
#Plot Primal gap in terms of time.
plt.semilogy(timingAFW, [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = int(len(timingAFW)/10), label = 'AFW')
plt.semilogy(timingPFW, [(x - fValOpt) for x in fValPFW], '-D', color = 'g', markevery = int(len(timingPFW)/10), label = 'PFW')
plt.semilogy(timingLaCGAFW, [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = int(len(timingLaCGAFW)/10), label = 'LaCG-AFW')
plt.semilogy(timingLaCGPFW, [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = int(len(timingLaCGPFW)/10), label = 'LaCG-PFW')
plt.semilogy(timingCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.semilogy(tCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.xlabel(r't[s]')
plt.grid()
plt.show()
plt.close()
# #Generate a timestamp for the example and save data
# ts = time.time()
# timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S').replace(' ', '-').replace(':', '-')
# with open(os.path.join(os.getcwd(), "MIPLIB_" + str(timestamp) + "_Mu" + str(MuVal) + "_L" + str(LVal)+ "_Size" + str(size) + "_TypeStep_" + typeOfStep + ".txt"), 'w') as f:
# f.write("size:\t" + str(len(x_0)) + "\n")
# f.write("Mu:\t" + str(fun.smallestEig())+ "\n")
# f.write("LVal:\t" + str(fun.largestEig())+ "\n")
# f.write("Tolerance:\t" + str(tolerance)+ "\n")
# f.write("Optimum:\t" + str(fValOpt)+ "\n")
# #Output the FW Gap.
# f.write("AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetAFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetPFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("Lazy AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetLazy).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCG) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW Lazy"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGLazy) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write("CGS"+ "\n")
# f.write(str([x - fValOpt for x in fValCGS]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingCGS) .replace("[", "").replace("]", "") + "\n")
# f.write(str(iterationCGS) .replace("[", "").replace("]", "") + "\n")
# f.write("Catalyst"+ "\n")
# f.write(str([x - fValOpt for x in fCatalyst]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(tCatalyst) .replace("[", "").replace("]", "") + "\n")
# f.write(str(itCatalyst) .replace("[", "").replace("]", "") + "\n")
"""
----------------------------Traffic Congestion POLYTOPE--------------------------
"""
TIME_LIMIT_REFERENCE_SOL = int(1800)
TIME_LIMIT = int(800)
MuVal = 0.1
LVal = 10.0
#Second feasible region.
file = 'road_paths_01_DC_a.lp'
tolerance = 2e-1
pathfile = os.path.join(os.getcwd(), 'TrafficNetwork', file)
feasibleRegion = GurobiPolytope(pathfile)
size = feasibleRegion.dim()
#Initial point
x_0 = feasibleRegion.initialPoint()
S_0 = [x_0]
alpha_0 = [1]
xOpt = np.random.rand(size)
#Create objective function.
fun = funcQuadraticDiag(size, xOpt, Mu = MuVal, L = LVal)
#Decomposition Invariant CG
print("Running algorithm to find optimum to high accuracy.")
TIME_LIMIT = int(4*3600)
print("Finding optimal solution to high accuracy using DIPFW or Lazy AFW.")
xTest, FWGapTest, fValTest, timingTest, activeSetTest = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance/2.0, TIME_LIMIT_REFERENCE_SOL, FWVariant = "Lazy", typeStep = "EL", criterion = "DG")
fValOpt = fValTest[-1]
tolerance = min(np.asarray(FWGapTest))
#Conditional Gradient Sliding.
print("Running CGS.")
CGS = CGS()
xCGS, FWGapCGS, fValCGS, timingCGS, iterationCGS = CGS.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, criterion = "PG", criterionRef = fValOpt)
#Catalyst augmented
print("Running Catalyst-augmented AFW.")
funCat = funcAccelSchemeDiag(len(x_0), fun.returnM(), fun.returnb(), fun.largestEig(), fun.smallestEig())
CatalystAFW = catalystScheme()
xCatalyst, FWCatalyst, fCatalyst, tCatalyst, itCatalyst = CatalystAFW.run(x_0, funCat, feasibleRegion, tolerance, TIME_LIMIT)
#LaCG
print("Running LaCG-AFW.")
LaCGAway = LaCG()
xLaCGAFW, FWGapLaCGAFW, fValLaCGAFW, timingLaCGAFW, activeLaCGAFW = LaCGAway.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCG = LaCGAway.returnRestarts()
restartsLaCG[:] = np.asarray([x - 1 for x in restartsLaCG])
#LaCG Lazy
print("Running LaCG-AFW Lazy.")
LaCGAwayLazy = LaCG()
xLaCGAFWLazy, FWGapLaCGAFWLazy, fValLaCGAFWLazy, timingLaCGAFWLazy, activeLaCGAFWLazy = LaCGAwayLazy.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGLazy = LaCGAwayLazy.returnRestarts()
restartsLaCGLazy[:] = np.asarray([x - 1 for x in restartsLaCGLazy])
#LaCG PFW
print("Running LaCG-PFW.")
LaCGPairwise = LaCG()
xLaCGPFW, FWGapLaCGPFW, fValLaCGPFW, timingLaCGPFW, activeLaCGPFW = LaCGPairwise.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGPFW = LaCGPairwise.returnRestarts()
restartsLaCGPFW[:] = np.asarray([x - 1 for x in restartsLaCGPFW])
#Vanilla PFW
print("Running PFW.")
xPFW, FWGapPFW, fValPFW, timingPFW, activeSetAFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Vanilla AFW
print("Running AFW.")
xAFW, FWGapAFW, fValAFW, timingAFW, activeSetPFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Run Lazy AFW
print("Running Lazy AFW.")
xAFWLazy, FWGapAFWLazy, fValAFWLazy, timingAFWLazy, activeSetLazy = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Plot primal gap in terms of iteration.
plt.loglog(np.arange(len(fValAFW)), [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = np.logspace(0, np.log10(len(fValAFW)-1), 10).astype(int).tolist(), label = 'AFW')
plt.loglog(np.arange(len(fValPFW)), [(x - fValOpt) for x in fValPFW], '-D', color = 'c', markevery = np.logspace(0, np.log10(len(fValPFW)-1), 10).astype(int).tolist(), label = 'PFW')
plt.loglog(np.arange(len(fValLaCGAFW)), [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = np.logspace(0, np.log10(len(fValLaCGAFW)-1), 10).astype(int).tolist(), label = 'LaCG-AFW')
plt.loglog(np.arange(len(fValLaCGPFW)), [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = np.logspace(0, np.log10(len(fValLaCGPFW)-1), 10).astype(int).tolist(), label = 'LaCG-PFW')
plt.loglog(iterationCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.loglog(itCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.xlabel(r'$k$')
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.grid()
plt.show()
plt.close()
#Plot Primal gap in terms of time.
plt.semilogy(timingAFW, [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = int(len(timingAFW)/10), label = 'AFW')
plt.semilogy(timingPFW, [(x - fValOpt) for x in fValPFW], '-D', color = 'g', markevery = int(len(timingPFW)/10), label = 'PFW')
plt.semilogy(timingLaCGAFW, [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = int(len(timingLaCGAFW)/10), label = 'LaCG-AFW')
plt.semilogy(timingLaCGPFW, [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = int(len(timingLaCGPFW)/10), label = 'LaCG-PFW')
plt.semilogy(timingCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.semilogy(tCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.xlabel(r't[s]')
plt.grid()
plt.show()
plt.close()
# #Generate a timestamp for the example and save data
# ts = time.time()
# timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S').replace(' ', '-').replace(':', '-')
# with open(os.path.join(os.getcwd(), "VideoColocalization_" + str(timestamp) + "_Mu" + str(MuVal) + "_L" + str(LVal)+ "_Size" + str(size) + "_TypeStep_" + typeOfStep + ".txt"), 'w') as f:
# f.write("size:\t" + str(len(x_0)) + "\n")
# f.write("Mu:\t" + str(fun.smallestEig())+ "\n")
# f.write("LVal:\t" + str(fun.largestEig())+ "\n")
# f.write("Tolerance:\t" + str(tolerance)+ "\n")
# f.write("Optimum:\t" + str(fValOpt)+ "\n")
# #Output the FW Gap.
# f.write("AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetAFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetPFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("Lazy AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetLazy).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCG) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW Lazy"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGLazy) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write("CGS"+ "\n")
# f.write(str([x - fValOpt for x in fValCGS]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingCGS) .replace("[", "").replace("]", "") + "\n")
# f.write(str(iterationCGS) .replace("[", "").replace("]", "") + "\n")
# f.write("Catalyst"+ "\n")
# f.write(str([x - fValOpt for x in fCatalyst]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(tCatalyst) .replace("[", "").replace("]", "") + "\n")
# f.write(str(itCatalyst) .replace("[", "").replace("]", "") + "\n")
"""
------------------------------VideoColocalization POLYTOPE----------------------------
"""
from feasibleRegions import flowPolytope
TIME_LIMIT_REFERENCE_SOL = int(1800)
TIME_LIMIT = int(1800)
MuVal = 0.1
LVal = 10.0
tolerance = 1.0e-4
typeOfStep = "SS"
feasibleRegion = flowPolytope(15, 15, typeGraph = "Structured")
size = feasibleRegion.dim()
#Initial point
x_0 = feasibleRegion.initialPoint()
S_0 = [x_0]
alpha_0 = [1]
xOpt = np.zeros(size)
matrix = randomPSDGeneratorSparse(size, 0.01)
LVal, MuVal = calculateEigenvalues(matrix)
"""# CREATE MATRIX AND EXPORT."""
#Create a matrix and export it.
ts = time.time()
timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S').replace(' ', '-').replace(':', '-')
fun = funcQuadratic(size, csc_matrix(matrix), xOpt, Mu = MuVal, L = LVal)
"""# SOLVE AND EXPORT THE SOLUTION."""
#Decomposition Invariant CG
print("Running algorithm to find optimum to high accuracy.")
TIME_LIMIT = int(1800)
xTest, FWGapTest, fValTest, timingTest = DIPFW(x_0, fun, feasibleRegion, tolerance/2.0, TIME_LIMIT_REFERENCE_SOL, typeStep = "EL", criterion = "DG")
fValOpt = fValTest[-1]
tolerance = min(np.asarray(FWGapTest))
TIME_LIMIT = int(1200)
#Conditional Gradient Sliding.
print("Running CGS.")
CGS = CGS()
xCGS, FWGapCGS, fValCGS, timingCGS, iterationCGS = CGS.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, criterion = "PG", criterionRef = fValOpt)
#Catalyst augmented
print("Running Catalyst-augmented AFW.")
funCat = funcAccelScheme(len(x_0), matrix, fun.returnb(), fun.largestEig(), fun.smallestEig())
CatalystAFW = catalystScheme()
xCatalyst, FWCatalyst, fCatalyst, tCatalyst, itCatalyst = CatalystAFW.run(x_0, funCat, feasibleRegion, tolerance, TIME_LIMIT)
#Decomposition Invariant CG
print("\nRunning DICG.")
xDICG, FWGapDICG, fValDICG, timingDICG = DIPFW(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#LaCG
print("Running LaCG-AFW.")
LaCGAway = LaCG()
xLaCGAFW, FWGapLaCGAFW, fValLaCGAFW, timingLaCGAFW, activeLaCGAFW = LaCGAway.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCG = LaCGAway.returnRestarts()
restartsLaCG[:] = np.asarray([x - 1 for x in restartsLaCG])
#LaCG Lazy
print("Running LaCG-AFW Lazy.")
LaCGAwayLazy = LaCG()
xLaCGAFWLazy, FWGapLaCGAFWLazy, fValLaCGAFWLazy, timingLaCGAFWLazy, activeLaCGAFWLazy = LaCGAwayLazy.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGLazy = LaCGAwayLazy.returnRestarts()
restartsLaCGLazy[:] = np.asarray([x - 1 for x in restartsLaCGLazy])
#LaCG PFW
print("Running LaCG-PFW.")
LaCGPairwise = LaCG()
xLaCGPFW, FWGapLaCGPFW, fValLaCGPFW, timingLaCGPFW, activeLaCGPFW = LaCGPairwise.run(x_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterionRef = fValOpt)
restartsLaCGPFW = LaCGPairwise.returnRestarts()
restartsLaCGPFW[:] = np.asarray([x - 1 for x in restartsLaCGPFW])
#Vanilla PFW
print("Running PFW.")
xPFW, FWGapPFW, fValPFW, timingPFW, activeSetAFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "PFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Vanilla AFW
print("Running AFW.")
xAFW, FWGapAFW, fValAFW, timingAFW, activeSetPFW = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "AFW", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Run Lazy AFW
print("Running Lazy AFW.")
xAFWLazy, FWGapAFWLazy, fValAFWLazy, timingAFWLazy, activeSetLazy = runFW(x_0, S_0, alpha_0, fun, feasibleRegion, tolerance, TIME_LIMIT, FWVariant = "Lazy", typeStep = typeOfStep, criterion = "PG", criterionRef = fValOpt)
#Plot primal gap in terms of iteration.
plt.loglog(np.arange(len(fValAFW)), [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = np.logspace(0, np.log10(len(fValAFW)-1), 10).astype(int).tolist(), label = 'AFW')
plt.loglog(np.arange(len(fValPFW)), [(x - fValOpt) for x in fValPFW], '-D', color = 'c', markevery = np.logspace(0, np.log10(len(fValPFW)-1), 10).astype(int).tolist(), label = 'PFW')
plt.loglog(np.arange(len(fValAFWLazy)), [(x - fValOpt) for x in fValAFWLazy], '--*', color = 'b', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValAFWLazy)-1), 10).astype(int).tolist(), label = 'AFW (L)')
plt.loglog(np.arange(len(fValLaCGAFW)), [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = np.logspace(0, np.log10(len(fValLaCGAFW)-1), 10).astype(int).tolist(), label = 'LaCG-AFW')
plt.loglog(np.arange(len(fValLaCGPFW)), [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = np.logspace(0, np.log10(len(fValLaCGPFW)-1), 10).astype(int).tolist(), label = 'LaCG-PFW')
plt.loglog(np.arange(len(fValLaCGAFWLazy)), [(x - fValOpt) for x in fValLaCGAFWLazy], '--o', color = 'k', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValLaCGAFWLazy)-1), 10).astype(int).tolist(), label = 'LaCG-AFW (L)')
plt.loglog(np.arange(len(fValDICG)), [(x - fValOpt) for x in fValDICG], '-s', color = 'r', markerfacecolor='none', markevery = np.logspace(0, np.log10(len(fValDICG)-1), 10).astype(int).tolist(), label = 'DICG')
plt.loglog(iterationCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.loglog(itCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.xlabel(r'$k$')
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.grid()
plt.show()
plt.close()
#Plot Primal gap in terms of time.
plt.semilogy(timingAFW, [(x - fValOpt) for x in fValAFW], '-*', color = 'b', markevery = int(len(timingAFW)/10), label = 'AFW')
plt.semilogy(timingPFW, [(x - fValOpt) for x in fValPFW], '-D', color = 'g', markevery = int(len(timingPFW)/10), label = 'PFW')
plt.semilogy(timingLaCGAFW, [(x - fValOpt) for x in fValLaCGAFW], '-o', color = 'k', markevery = int(len(timingLaCGAFW)/10), label = 'LaCG-AFW')
plt.semilogy(timingLaCGPFW, [(x - fValOpt) for x in fValLaCGPFW], '-^', color = 'g', markevery = int(len(timingLaCGPFW)/10), label = 'LaCG-PFW')
plt.semilogy(timingDICG, [(x - fValOpt) for x in fValDICG], '-s', color = 'r', markevery = int(len(timingDICG)/10), label = 'DICG')
plt.semilogy(timingCGS, [(x - fValOpt) for x in fValCGS], color = 'y', label = 'CGS')
plt.semilogy(tCatalyst, [(x - fValOpt) for x in fCatalyst], ':', color = 'm', label = 'Catalyst')
plt.legend()
plt.ylabel(r'$f(x_{k}) - f^{*}$')
plt.xlabel(r't[s]')
plt.grid()
plt.show()
plt.close()
# #Generate a timestamp for the example and save data
# ts = time.time()
# timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S').replace(' ', '-').replace(':', '-')
# with open(os.path.join(os.getcwd(), "VideoColocalization_" + str(timestamp) + "_Mu" + str(MuVal) + "_L" + str(LVal)+ "_Size" + str(size) + "_TypeStep_" + typeOfStep + ".txt"), 'w') as f:
# f.write("size:\t" + str(len(x_0)) + "\n")
# f.write("Mu:\t" + str(fun.smallestEig())+ "\n")
# f.write("LVal:\t" + str(fun.largestEig())+ "\n")
# f.write("Tolerance:\t" + str(tolerance)+ "\n")
# f.write("Optimum:\t" + str(fValOpt)+ "\n")
# #Output the FW Gap.
# f.write("AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetAFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetPFW).replace("[", "").replace("]", "") + "\n")
# #Output the FW Gap.
# f.write("Lazy AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(activeSetLazy).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCG) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-PFW"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGPFW]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGPFW) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGPFW).replace("[", "").replace("]", "") + "\n")
# f.write("LaCG-AFW Lazy"+ "\n")
# f.write(str([x - fValOpt for x in fValLaCGAFWLazy]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write(str(restartsLaCGLazy) .replace("[", "").replace("]", "") + "\n")
# f.write(str(activeLaCGAFWLazy).replace("[", "").replace("]", "") + "\n")
# f.write("CGS"+ "\n")
# f.write(str([x - fValOpt for x in fValCGS]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapCGS).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingCGS) .replace("[", "").replace("]", "") + "\n")
# f.write(str(iterationCGS) .replace("[", "").replace("]", "") + "\n")
# f.write("DICG"+ "\n")
# f.write(str([x - fValOpt for x in fValDICG]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fValDICG).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWGapDICG).replace("[", "").replace("]", "") + "\n")
# f.write(str(timingDICG) .replace("[", "").replace("]", "") + "\n")
# f.write("Catalyst"+ "\n")
# f.write(str([x - fValOpt for x in fCatalyst]).replace("[", "").replace("]", "") + "\n")
# f.write(str(fCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(FWCatalyst).replace("[", "").replace("]", "") + "\n")
# f.write(str(tCatalyst) .replace("[", "").replace("]", "") + "\n")
# f.write(str(itCatalyst) .replace("[", "").replace("]", "") + "\n")