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l1scenario.m
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function PARAM = l1scenario(s)
if ( s == 1 )
PARAM.UseAR = 'no';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 2e-3;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 6;
% PARAM.NOEXP = 500;
PARAM.NOEXP = 1;
% PARAM.NoIter = 5e4;
PARAM.NoIter = 5e3;
PARAM.D = 100;
PARAM.SparsityPerc = 1/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-1;
% l0-RLS.
PARAM.deltal0RLS = 1e-1;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-1;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 5e2;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-2;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e1;
PARAM.rhoADMM = 1e1;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 5e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.9;
PARAM.gammaI = 1e0;
PARAM.LipSFMI = 90e-4;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.9;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 90e-4;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 2 )
PARAM.UseAR = 'no';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 2e-3;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 6;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-1;
% l0-RLS.
PARAM.deltal0RLS = 1e-1;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-1;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 5e2;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-2;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e1;
PARAM.rhoADMM = 1e1;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 5e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 1e2;
PARAM.LipSFMI = 7e-3;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 9e-3;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 3 )
PARAM.UseAR = 'no';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 5e-3;
PARAM.SNRdB = 10;
PARAM.SNRdBAR = 6;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 1/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e3;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-2;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e1;
PARAM.rhoADMM = 1e1;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 5e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.9;
PARAM.gammaI = 1e0;
PARAM.LipSFMI = 10e-3;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.9;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 10e-3;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 4 )
PARAM.UseAR = 'no';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 5e-3;
PARAM.SNRdB = 10;
PARAM.SNRdBAR = 6;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e3;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-2;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e1;
PARAM.rhoADMM = 1e1;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 1e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 5e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 5e2;
PARAM.LipSFMI = 20e-4;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 40e-4;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 5 )
PARAM.UseAR = 'yes';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 25e-4;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 5;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 1/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e3;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-3;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e2;
PARAM.rhoADMM = 1e2;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (2e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 1e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.9;
PARAM.gammaI = 5e2;
PARAM.LipSFMI = 65e-4;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.9;
PARAM.muSFMIV = 1e0;
PARAM.LipSFMIV = 30e-4;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 6 )
PARAM.UseAR = 'yes';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 40e-4;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 5;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e3;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-3;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e2;
PARAM.rhoADMM = 1e2;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (2e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 1e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 5e2;
PARAM.LipSFMI = 60e-4;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.6;
PARAM.muSFMIV = 1e0;
PARAM.LipSFMIV = 22e-4;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 7 )
PARAM.UseAR = 'yes';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 10e-3;
PARAM.SNRdB = 10;
PARAM.SNRdBAR = 5;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 1/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e3;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-3;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e2;
PARAM.rhoADMM = 1e2;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (5e-5)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 1e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.9;
PARAM.gammaI = 5e2;
PARAM.LipSFMI = 50e-4;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 20e-4;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 8 )
PARAM.UseAR = 'yes';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 10e-3;
PARAM.SNRdB = 10;
PARAM.SNRdBAR = 5;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e3;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e3;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-3;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e2;
PARAM.rhoADMM = 1e2;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 1e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 5e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 5e2;
PARAM.LipSFMI = 20e-4;
% Standard loss.
PARAM.alphaII = 0.9;
PARAM.LipSFMII = 1e-1;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 5e-4;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 9 )
PARAM.UseAR = 'no';
PARAM.UseNoiseInModel = 'no';
PARAM.WeightLossReg = 1e-20;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 5;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 1e2;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e-1;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 1e2;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e-1;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-2;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e1;
PARAM.rhoADMM = 1e1;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-2)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 1e-1;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 1e1;
PARAM.LipSFMI = 40e-3;
% Standard loss.
PARAM.alphaII = 0.5;
PARAM.LipSFMII = 40e-3;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 30e-3;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 10 )
PARAM.UseAR = 'yes';
PARAM.UseNoiseInModel = 'no';
PARAM.WeightLossReg = 1e-20;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 5;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e4;
PARAM.D = 100;
PARAM.SparsityPerc = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 1e2;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e-1;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 1e2;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e-1;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-3;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e2;
PARAM.rhoADMM = 1e2;
% ASVB.
PARAM.lambdaASVB = 1-(1e-4);
PARAM.ConstASVB = 5e-6;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-2)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 1e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 1e0;
PARAM.LipSFMI = 30e-3;
% Standard loss.
PARAM.alphaII = 0.5;
PARAM.LipSFMII = 30e-3;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 9e-3;
PARAM.ConstAboveEigenV = 1;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
elseif ( s == 11 )
PARAM.UseAR = 'no';
PARAM.UseNoiseInModel = 'yes';
PARAM.WeightLossReg = 5e-3;
PARAM.SNRdB = 20;
PARAM.SNRdBAR = 6;
PARAM.NOEXP = 500;
PARAM.NoIter = 5e3;
PARAM.SwitchPoint = 2.5e3;
PARAM.D = 100;
PARAM.SparsityPercA = 1/100;
PARAM.SparsityPercB = 10/100;
% PARAM.VarNonZeroValuesTarget = .1;
PARAM.delta = .9925;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% RLS.
PARAM.epsilonRLS = 1e-2;
% l0-RLS.
PARAM.deltal0RLS = 1e-2;
PARAM.betal0RLS = 5e1;
PARAM.UseConstantGammal0RLS = 'yes';
PARAM.ConstantGammal0RLS = 1e2;
PARAM.Multiplygammal0RLSby = 1;
PARAM.lambdal0RLS = 1-(1e-5);
% l1-RLS.
PARAM.deltal1RLS = 1e-2;
PARAM.betal1RLS = 5e1;
PARAM.UseConstantGammal1RLS = 'yes';
PARAM.ConstantGammal1RLS = 1e2;
PARAM.Multiplygammal1RLSby = 1;
PARAM.lambdal1RLS = 1-(1e-5);
% Prox-SVRG.
PARAM.mSVRG = 100;
PARAM.etaSVRG = 1e-2;
% SVRG-ADMM.
PARAM.mADMM = 5;
PARAM.bADMM = 100;
PARAM.etaADMM = 1e1;
PARAM.rhoADMM = 1e1;
% ASVB.
PARAM.lambdaASVB = 1-(1e-2);
PARAM.ConstASVB = 1e-7;
PARAM.ARcoeff = sqrt(1-10^(-PARAM.SNRdBAR/10));
if (strcmp(PARAM.UseAR,'yes'))
PARAM.LipCoeff = 1/(1-PARAM.ARcoeff^2)+(1e-2);
else
PARAM.LipCoeff = 1+(1e-2);
end
% AC-SA. See (33):
% PARAM.GammaACSA = min(1e-4,1/2/PARAM.LipCoeff);
PARAM.GammaACSA = (1e-4)/2/PARAM.LipCoeff;
% SDA. See (6).
PARAM.gammaSDA = 5e-2;
PARAM.lambdaSDA = PARAM.WeightLossReg;
% S-FM-HSDM.
% Adaptive constraints (T = Prox).
PARAM.alphaI = 0.5;
PARAM.gammaI = 1e0;
PARAM.LipSFMI = 1500e-4;
% Standard loss.
PARAM.alphaII = 0.5;
PARAM.LipSFMII = 5e-2;
% Adaptive constraints (T = Grad).
PARAM.alphaIV = 0.5;
PARAM.muSFMIV = 1;
PARAM.LipSFMIV = 2000e-4;
PARAM.ConstAboveEigenV = 5;
% Adaptive constraints (T = Proj).
% PARAM.LipSFMIII = PARAM.LipSFM;
end
end