Mercurial > hg > pycsalgos
diff pyCSalgos/NESTA/NESTA.py @ 46:88f0ebe1667a
Finished NESTA implementation. Not tested yet
| author | nikcleju |
|---|---|
| date | Tue, 29 Nov 2011 22:06:20 +0000 |
| parents | 7524d7749456 |
| children | e6a5f2173015 |
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--- a/pyCSalgos/NESTA/NESTA.py Tue Nov 29 18:13:17 2011 +0000 +++ b/pyCSalgos/NESTA/NESTA.py Tue Nov 29 22:06:20 2011 +0000 @@ -327,7 +327,7 @@ else: s = numpy.diag(S) - #V = USV.V; + V = USV['V']; #else # error('opts.USV must be a structure'); #end @@ -637,3 +637,1111 @@ cnt = cnt+1; #end #end + + + +#function [xk,niter,residuals,outputData,opts] = Core_Nesterov(A,At,b,mu,delta,opts) +def Core_Nesterov(A,At,b,mu,delta,opts): +# [xk,niter,residuals,outputData,opts] =Core_Nesterov(A,At,b,mu,delta,opts) +# +# Solves a L1 minimization problem under a quadratic constraint using the +# Nesterov algorithm, without continuation: +# +# min_x || U x ||_1 s.t. ||y - Ax||_2 <= delta +# +# If continuation is desired, see the function NESTA.m +# +# The primal prox-function is also adapted by accounting for a first guess +# xplug that also tends towards x_muf +# +# The observation matrix A is a projector +# +# Inputs: A and At - measurement matrix and adjoint (either a matrix, in which +# case At is unused, or function handles). m x n dimensions. +# b - Observed data, a m x 1 array +# muf - The desired value of mu at the last continuation step. +# A smaller mu leads to higher accuracy. +# delta - l2 error bound. This enforces how close the variable +# must fit the observations b, i.e. || y - Ax ||_2 <= delta +# If delta = 0, enforces y = Ax +# Common heuristic: delta = sqrt(m + 2*sqrt(2*m))*sigma; +# where sigma=std(noise). +# opts - +# This is a structure that contains additional options, +# some of which are optional. +# The fieldnames are case insensitive. Below +# are the possible fieldnames: +# +# opts.xplug - the first guess for the primal prox-function, and +# also the initial point for xk. By default, xplug = At(b) +# opts.U and opts.Ut - Analysis/Synthesis operators +# (either matrices of function handles). +# opts.normU - if opts.U is provided, this should be norm(U) +# opts.maxiter - max number of iterations in an inner loop. +# default is 10,000 +# opts.TolVar - tolerance for the stopping criteria +# opts.stopTest - which stopping criteria to apply +# opts.stopTest == 1 : stop when the relative +# change in the objective function is less than +# TolVar +# opts.stopTest == 2 : stop with the l_infinity norm +# of difference in the xk variable is less +# than TolVar +# opts.TypeMin - if this is 'L1' (default), then +# minimizes a smoothed version of the l_1 norm. +# If this is 'tv', then minimizes a smoothed +# version of the total-variation norm. +# The string is case insensitive. +# opts.Verbose - if this is 0 or false, then very +# little output is displayed. If this is 1 or true, +# then output every iteration is displayed. +# If this is a number p greater than 1, then +# output is displayed every pth iteration. +# opts.fid - if this is 1 (default), the display is +# the usual Matlab screen. If this is the file-id +# of a file opened with fopen, then the display +# will be redirected to this file. +# opts.errFcn - if this is a function handle, +# then the program will evaluate opts.errFcn(xk) +# at every iteration and display the result. +# ex. opts.errFcn = @(x) norm( x - x_true ) +# opts.outFcn - if this is a function handle, +# then then program will evaluate opts.outFcn(xk) +# at every iteration and save the results in outputData. +# If the result is a vector (as opposed to a scalar), +# it should be a row vector and not a column vector. +# ex. opts.outFcn = @(x) [norm( x - xtrue, 'inf' ),... +# norm( x - xtrue) / norm(xtrue)] +# opts.AAtinv - this is an experimental new option. AAtinv +# is the inverse of AA^*. This allows the use of a +# matrix A which is not a projection, but only +# for the noiseless (i.e. delta = 0) case. +# If the SVD of A is U*S*V', then AAtinv = U*(S^{-2})*U'. +# opts.USV - another experimental option. This supercedes +# the AAtinv option, so it is recommended that you +# do not define AAtinv. This allows the use of a matrix +# A which is not a projection, and works for the +# noisy ( i.e. delta > 0 ) case. +# opts.USV should contain three fields: +# opts.USV.U is the U from [U,S,V] = svd(A) +# likewise, opts.USV.S and opts.USV.V are S and V +# from svd(A). S may be a matrix or a vector. +# Outputs: +# xk - estimate of the solution x +# niter - number of iterations +# residuals - first column is the residual at every step, +# second column is the value of f_mu at every step +# outputData - a matrix, where each row r is the output +# from opts.outFcn, if supplied. +# opts - the structure containing the options that were used +# +# Written by: Jerome Bobin, Caltech +# Email: bobin@acm.caltech.edu +# Created: February 2009 +# Modified: May 2009, Jerome Bobin and Stephen Becker, Caltech +# Modified: Nov 2009, Stephen Becker +# +# NESTA Version 1.1 +# See also NESTA + +#---- Set defaults +# opts = []; + + #--------------------- + # Original Matab code: + + #fid = setOpts('fid',1); + #function printf(varargin), fprintf(fid,varargin{:}); end + #maxiter = setOpts('maxiter',10000,0); + #TolVar = setOpts('TolVar',1e-5); + #TypeMin = setOpts('TypeMin','L1'); + #Verbose = setOpts('Verbose',true); + #errFcn = setOpts('errFcn',[]); + #outFcn = setOpts('outFcn',[]); + #stopTest = setOpts('stopTest',1,1,2); + #U = setOpts('U', @(x) x ); + #if ~isa(U,'function_handle') + # Ut = setOpts('Ut',[]); + #else + # Ut = setOpts('Ut', @(x) x ); + #end + #xplug = setOpts('xplug',[]); + #normU = setOpts('normU',1); + # + #if delta < 0, error('delta must be greater or equal to zero'); end + # + #if isa(A,'function_handle') + # Atfun = At; + # Afun = A; + #else + # Atfun = @(x) A'*x; + # Afun = @(x) A*x; + #end + #Atb = Atfun(b); + # + #AAtinv = setOpts('AAtinv',[]); + #USV = setOpts('USV',[]); + #if ~isempty(USV) + # if isstruct(USV) + # Q = USV.U; # we can't use "U" as the variable name + # # since "U" already refers to the analysis operator + # S = USV.S; + # if isvector(S), s = S; S = diag(s); + # else s = diag(S); end + # V = USV.V; + # else + # error('opts.USV must be a structure'); + # end + # if isempty(AAtinv) + # AAtinv = Q*diag( s.^(-2) )*Q'; + # end + #end + ## --- for A not a projection (experimental) + #if ~isempty(AAtinv) + # if isa(AAtinv,'function_handle') + # AAtinv_fun = AAtinv; + # else + # AAtinv_fun = @(x) AAtinv * x; + # end + # + # AtAAtb = Atfun( AAtinv_fun(b) ); + # + #else + # # We assume it's a projection + # AtAAtb = Atb; + # AAtinv_fun = @(x) x; + #end + # + #if isempty(xplug) + # xplug = AtAAtb; + #end + # + ##---- Initialization + #N = length(xplug); + #wk = zeros(N,1); + #xk = xplug; + # + # + ##---- Init Variables + #Ak= 0; + #Lmu = normU/mu; + #yk = xk; + #zk = xk; + #fmean = realmin/10; + #OK = 0; + #n = floor(sqrt(N)); + # + ##---- Computing Atb + #Atb = Atfun(b); + #Axk = Afun(xk);# only needed if you want to see the residuals + ## Axplug = Axk; + # + # + ##---- TV Minimization + #if strcmpi(TypeMin,'TV') + # Lmu = 8*Lmu; + # Dv = spdiags([reshape([-ones(n-1,n); zeros(1,n)],N,1) ... + # reshape([zeros(1,n); ones(n-1,n)],N,1)], [0 1], N, N); + # Dh = spdiags([reshape([-ones(n,n-1) zeros(n,1)],N,1) ... + # reshape([zeros(n,1) ones(n,n-1)],N,1)], [0 n], N, N); + # D = sparse([Dh;Dv]); + #end + # + # + #Lmu1 = 1/Lmu; + ## SLmu = sqrt(Lmu); + ## SLmu1 = 1/sqrt(Lmu); + #lambdaY = 0; + #lambdaZ = 0; + # + ##---- setup data storage variables + #[DISPLAY_ERROR, RECORD_DATA] = deal(false); + #outputData = deal([]); + #residuals = zeros(maxiter,2); + #if ~isempty(errFcn), DISPLAY_ERROR = true; end + #if ~isempty(outFcn) && nargout >= 4 + # RECORD_DATA = true; + # outputData = zeros(maxiter, size(outFcn(xplug),2) ); + #end + # + #for k = 0:maxiter-1, + # + # #---- Dual problem + # + # if strcmpi(TypeMin,'L1') [df,fx,val,uk] = Perform_L1_Constraint(xk,mu,U,Ut);end + # + # if strcmpi(TypeMin,'TV') [df,fx] = Perform_TV_Constraint(xk,mu,Dv,Dh,D,U,Ut);end + # + # #---- Primal Problem + # + # #---- Updating yk + # + # # + # # yk = Argmin_x Lmu/2 ||x - xk||_l2^2 + <df,x-xk> s.t. ||b-Ax||_l2 < delta + # # Let xp be sqrt(Lmu) (x-xk), dfp be df/sqrt(Lmu), bp be sqrt(Lmu)(b- Axk) and deltap be sqrt(Lmu)delta + # # yk = xk + 1/sqrt(Lmu) Argmin_xp 1/2 || xp ||_2^2 + <dfp,xp> s.t. || bp - Axp ||_2 < deltap + # # + # + # + # cp = xk - 1/Lmu*df; # this is "q" in eq. (3.7) in the paper + # + # Acp = Afun( cp ); + # if ~isempty(AAtinv) && isempty(USV) + # AtAcp = Atfun( AAtinv_fun( Acp ) ); + # else + # AtAcp = Atfun( Acp ); + # end + # + # residuals(k+1,1) = norm( b-Axk); # the residual + # residuals(k+1,2) = fx; # the value of the objective + # #--- if user has supplied a function, apply it to the iterate + # if RECORD_DATA + # outputData(k+1,:) = outFcn(xk); + # end + # + # if delta > 0 + # if ~isempty(USV) + # # there are more efficient methods, but we're assuming + # # that A is negligible compared to U and Ut. + # # Here we make the change of variables x <-- x - xk + # # and df <-- df/L + # dfp = -Lmu1*df; Adfp = -(Axk - Acp); + # bp = b - Axk; + # deltap = delta; + # # Check if we even need to project: + # if norm( Adfp - bp ) < deltap + # lambdaY = 0; projIter = 0; + # # i.e. projection = dfp; + # yk = xk + dfp; + # Ayk = Axk + Adfp; + # else + # lambdaY_old = lambdaY; + # [projection,projIter,lambdaY] = fastProjection(Q,S,V,dfp,bp,... + # deltap, .999*lambdaY_old ); + # if lambdaY > 0, disp('lambda is positive!'); keyboard; end + # yk = xk + projection; + # Ayk = Afun(yk); + # # DEBUGGING + ## if projIter == 50 + ## fprintf('\n Maxed out iterations at y\n'); + ## keyboard + ## end + # end + # else + # lambda = max(0,Lmu*(norm(b-Acp)/delta - 1));gamma = lambda/(lambda + Lmu); + # yk = lambda/Lmu*(1-gamma)*Atb + cp - gamma*AtAcp; + # # for calculating the residual, we'll avoid calling A() + # # by storing A(yk) here (using A'*A = I): + # Ayk = lambda/Lmu*(1-gamma)*b + Acp - gamma*Acp; + # end + # else + # # if delta is 0, the projection is simplified: + # yk = AtAAtb + cp - AtAcp; + # Ayk = b; + # end + # + # # DEBUGGING + ## if norm( Ayk - b ) > (1.05)*delta + ## fprintf('\nAyk failed projection test\n'); + ## keyboard; + ## end + # + # #--- Stopping criterion + # qp = abs(fx - mean(fmean))/mean(fmean); + # + # switch stopTest + # case 1 + # # look at the relative change in function value + # if qp <= TolVar && OK; break;end + # if qp <= TolVar && ~OK; OK=1; end + # case 2 + # # look at the l_inf change from previous iterate + # if k >= 1 && norm( xk - xold, 'inf' ) <= TolVar + # break + # end + # end + # fmean = [fx,fmean]; + # if (length(fmean) > 10) fmean = fmean(1:10);end + # + # + # + # #--- Updating zk + # + # apk =0.5*(k+1); + # Ak = Ak + apk; + # tauk = 2/(k+3); + # + # wk = apk*df + wk; + # + # # + # # zk = Argmin_x Lmu/2 ||b - Ax||_l2^2 + Lmu/2||x - xplug ||_2^2+ <wk,x-xk> + # # s.t. ||b-Ax||_l2 < delta + # # + # + # cp = xplug - 1/Lmu*wk; + # + # Acp = Afun( cp ); + # if ~isempty( AAtinv ) && isempty(USV) + # AtAcp = Atfun( AAtinv_fun( Acp ) ); + # else + # AtAcp = Atfun( Acp ); + # end + # + # if delta > 0 + # if ~isempty(USV) + # # Make the substitution wk <-- wk/K + # + ## dfp = (xplug - Lmu1*wk); # = cp + ## Adfp= (Axplug - Acp); + # dfp = cp; Adfp = Acp; + # bp = b; + # deltap = delta; + ## dfp = SLmu*xplug - SLmu1*wk; + ## bp = SLmu*b; + ## deltap = SLmu*delta; + # + # # See if we even need to project: + # if norm( Adfp - bp ) < deltap + # zk = dfp; + # Azk = Adfp; + # else + # [projection,projIter,lambdaZ] = fastProjection(Q,S,V,dfp,bp,... + # deltap, .999*lambdaZ ); + # if lambdaZ > 0, disp('lambda is positive!'); keyboard; end + # zk = projection; + # # zk = SLmu1*projection; + # Azk = Afun(zk); + # + # # DEBUGGING: + ## if projIter == 50 + ## fprintf('\n Maxed out iterations at z\n'); + ## keyboard + ## end + # end + # else + # lambda = max(0,Lmu*(norm(b-Acp)/delta - 1));gamma = lambda/(lambda + Lmu); + # zk = lambda/Lmu*(1-gamma)*Atb + cp - gamma*AtAcp; + # # for calculating the residual, we'll avoid calling A() + # # by storing A(zk) here (using A'*A = I): + # Azk = lambda/Lmu*(1-gamma)*b + Acp - gamma*Acp; + # end + # else + # # if delta is 0, this is simplified: + # zk = AtAAtb + cp - AtAcp; + # Azk = b; + # end + # + # # DEBUGGING + ## if norm( Ayk - b ) > (1.05)*delta + ## fprintf('\nAzk failed projection test\n'); + ## keyboard; + ## end + # + # #--- Updating xk + # + # xkp = tauk*zk + (1-tauk)*yk; + # xold = xk; + # xk=xkp; + # Axk = tauk*Azk + (1-tauk)*Ayk; + # + # if ~mod(k,10), Axk = Afun(xk); end # otherwise slowly lose precision + # # DEBUG + ## if norm(Axk - Afun(xk) ) > 1e-6, disp('error with Axk'); keyboard; end + # + # #--- display progress if desired + # if ~mod(k+1,Verbose ) + # printf('Iter: #3d ~ fmu: #.3e ~ Rel. Variation of fmu: #.2e ~ Residual: #.2e',... + # k+1,fx,qp,residuals(k+1,1) ); + # #--- if user has supplied a function to calculate the error, + # # apply it to the current iterate and dislay the output: + # if DISPLAY_ERROR, printf(' ~ Error: #.2e',errFcn(xk)); end + # printf('\n'); + # end + # if abs(fx)>1e20 || abs(residuals(k+1,1)) >1e20 || isnan(fx) + # error('Nesta: possible divergence or NaN. Bad estimate of ||A''A||?'); + # end + # + #end + # + #niter = k+1; + # + ##-- truncate output vectors + #residuals = residuals(1:niter,:); + #if RECORD_DATA, outputData = outputData(1:niter,:); end + + # End of original Matab code + #--------------------- + + #fid = setOpts('fid',1); + #function printf(varargin), fprintf(fid,varargin{:}); end + opts,maxiter,userSet = setOpts(opts,'maxiter',10000,0); + opts,TolVar,userSet = setOpts(opts,'TolVar',1e-5); + opts,TypeMin,userSet = setOpts(opts,'TypeMin','L1'); + opts,Verbose,userSet = setOpts(opts,'Verbose',True); + opts,errFcn,userSet = setOpts(opts,'errFcn',None); + opts,outFcn,userSet = setOpts(opts,'outFcn',None); + opts,stopTest,userSet = setOpts(opts,'stopTest',1,1,2); + opts,U,userSet = setOpts(opts,'U',lambda x: x ); + #if ~isa(U,'function_handle') + if hasattr(U,'__call__'): + opts,Ut,userSet = setOpts(opts,'Ut',None); + else: + opts,Ut,userSet = setOpts(opts,'Ut', lambda x: x ); + #end + opts,xplug,userSet = setOpts(opts,'xplug',None); + opts,normU,userSet = setOpts(opts,'normU',1); + + if delta < 0: + print 'delta must be greater or equal to zero' + raise + + if hasattr(A,'__call__'): + Atfun = At; + Afun = A; + else: + Atfun = lambda x: numpy.dot(A.T,x) + Afun = lambda x: numpy.dot(A,x) + #end + Atb = Atfun(b); + + opts,AAtinv,userSet = setOpts(opts,'AAtinv',None); + opts,USV,userSet = setOpts(opts,'USV',None); + if USV is not None: + #if isstruct(USV) + Q = USV['U']; # we can't use "U" as the variable name + # since "U" already refers to the analysis operator + S = USV['S']; + #if isvector(S), s = S; S = diag(s); + #else s = diag(S); end + if S.ndim is 1: + s = S + else: + s = numpy.diag(S) + + V = USV['V']; + #else + # error('opts.USV must be a structure'); + #end + #if isempty(AAtinv) + if AAtinv is None: + #AAtinv = Q*diag( s.^(-2) )*Q'; + AAtinv = numpy.dot(Q, numpy.dot(numpy.diag(s ** -2), Q.T)) + #end + #end + # --- for A not a projection (experimental) + #if ~isempty(AAtinv) + if AAtinv is not None: + #if isa(AAtinv,'function_handle') + if hasattr(AAtinv, '__call__'): + AAtinv_fun = AAtinv; + else: + AAtinv_fun = lambda x: numpy.dot(AAtinv,x) + #end + + AtAAtb = Atfun( AAtinv_fun(b) ); + + else: + # We assume it's a projection + AtAAtb = Atb; + AAtinv_fun = lambda x: x; + #end + + if xplug == None: + xplug = AtAAtb.copy(); + #end + + #---- Initialization + #N = length(xplug); + N = len(xplug) + #wk = zeros(N,1); + wk = numpy.zeros(N) + xk = xplug.copy() + + + #---- Init Variables + Ak = 0.0; + Lmu = normU/mu; + yk = xk.copy(); + zk = xk.copy(); + fmean = numpy.finfo(float).tiny/10.0; + OK = 0; + n = math.floor(math.sqrt(N)); + + #---- Computing Atb + Atb = Atfun(b); + Axk = Afun(xk);# only needed if you want to see the residuals + # Axplug = Axk; + + + #---- TV Minimization + if TypeMin == 'TV': + print 'Nic:TODO: TV minimization not yet implemented!' + raise + #if strcmpi(TypeMin,'TV') + # Lmu = 8*Lmu; + # Dv = spdiags([reshape([-ones(n-1,n); zeros(1,n)],N,1) ... + # reshape([zeros(1,n); ones(n-1,n)],N,1)], [0 1], N, N); + # Dh = spdiags([reshape([-ones(n,n-1) zeros(n,1)],N,1) ... + # reshape([zeros(n,1) ones(n,n-1)],N,1)], [0 n], N, N); + # D = sparse([Dh;Dv]); + #end + + + Lmu1 = 1.0/Lmu; + # SLmu = sqrt(Lmu); + # SLmu1 = 1/sqrt(Lmu); + lambdaY = 0.; + lambdaZ = 0.; + + #---- setup data storage variables + #[DISPLAY_ERROR, RECORD_DATA] = deal(false); + DISPLAY_ERROR = False + RECORD_DATA = False + #outputData = deal([]); + outputData = None + residuals = numpy.zeros((maxiter,2)) + #if ~isempty(errFcn), DISPLAY_ERROR = true; end + if errFcn is not None: + DISPLAY_ERROR = True + #if ~isempty(outFcn) && nargout >= 4 + if outFcn is not None: # Output max number of arguments + RECORD_DATA = True + outputData = numpy.zeros(maxiter, outFcn(xplug).shape[1]); + #end + + #for k = 0:maxiter-1, + for k in numpy.arange(maxiter): + + #---- Dual problem + + #if strcmpi(TypeMin,'L1') [df,fx,val,uk] = Perform_L1_Constraint(xk,mu,U,Ut);end + if TypeMin == 'L1': + df,fx,val,uk = Perform_L1_Constraint(xk,mu,U,Ut) + + # Nic: TODO: TV not implemented yet ! + #if strcmpi(TypeMin,'TV') [df,fx] = Perform_TV_Constraint(xk,mu,Dv,Dh,D,U,Ut);end + + #---- Primal Problem + + #---- Updating yk + + # + # yk = Argmin_x Lmu/2 ||x - xk||_l2^2 + <df,x-xk> s.t. ||b-Ax||_l2 < delta + # Let xp be sqrt(Lmu) (x-xk), dfp be df/sqrt(Lmu), bp be sqrt(Lmu)(b- Axk) and deltap be sqrt(Lmu)delta + # yk = xk + 1/sqrt(Lmu) Argmin_xp 1/2 || xp ||_2^2 + <dfp,xp> s.t. || bp - Axp ||_2 < deltap + # + + + cp = xk - 1./Lmu*df; # this is "q" in eq. (3.7) in the paper + + Acp = Afun( cp ); + #if ~isempty(AAtinv) && isempty(USV) + if AAtinv is not None and USV is None: + AtAcp = Atfun( AAtinv_fun( Acp ) ); + else: + AtAcp = Atfun( Acp ); + #end + + #residuals(k+1,1) = norm( b-Axk); # the residual + residuals[k,0] = numpy.linalg.norm(b-Axk) + #residuals(k+1,2) = fx; # the value of the objective + residuals[k,1] = fx + #--- if user has supplied a function, apply it to the iterate + if RECORD_DATA: + outputData[k+1,:] = outFcn(xk); + #end + + if delta > 0: + #if ~isempty(USV) + if USV is not None: + # there are more efficient methods, but we're assuming + # that A is negligible compared to U and Ut. + # Here we make the change of variables x <-- x - xk + # and df <-- df/L + dfp = -Lmu1*df; + Adfp = -(Axk - Acp); + bp = b - Axk; + deltap = delta; + # Check if we even need to project: + if numpy.linalg.norm( Adfp - bp ) < deltap: + lambdaY = 0. + projIter = 0; + # i.e. projection = dfp; + yk = xk + dfp; + Ayk = Axk + Adfp; + else: + lambdaY_old = lambdaY.copy(); + #[projection,projIter,lambdaY] = fastProjection(Q,S,V,dfp,bp,deltap, .999*lambdaY_old ); + projection,projIter,lambdaY = fastProjection(Q,S,V,dfp,bp,deltap, .999*lambdaY_old ) + #if lambdaY > 0, disp('lambda is positive!'); keyboard; end + if lambdaY > 0: + print 'lambda is positive!' + raise + yk = xk + projection; + Ayk = Afun(yk); + # DEBUGGING + # if projIter == 50 + # fprintf('\n Maxed out iterations at y\n'); + # keyboard + # end + #end + else: + lambdaa = max(0,Lmu*(numpy.linalg.norm(b-Acp)/delta - 1)) + gamma = lambdaa/(lambdaa + Lmu); + yk = lambdaa/Lmu*(1-gamma)*Atb + cp - gamma*AtAcp; + # for calculating the residual, we'll avoid calling A() + # by storing A(yk) here (using A'*A = I): + Ayk = lambdaa/Lmu*(1-gamma)*b + Acp - gamma*Acp; + #end + else: + # if delta is 0, the projection is simplified: + yk = AtAAtb + cp - AtAcp; + Ayk = b.copy(); + #end + + # DEBUGGING + # if norm( Ayk - b ) > (1.05)*delta + # fprintf('\nAyk failed projection test\n'); + # keyboard; + # end + + #--- Stopping criterion + qp = abs(fx - numpy.mean(fmean))/numpy.mean(fmean); + + #switch stopTest + # case 1 + if stopTest == 1: + # look at the relative change in function value + #if qp <= TolVar && OK; break;end + if qp <= TolVar and OK: + break + #if qp <= TolVar && ~OK; OK=1; end + if qp <= TolVar and not OK: + OK = 1 + elif stopTest == 2: + # look at the l_inf change from previous iterate + if k >= 1 and numpy.linalg.norm( xk - xold, 'inf' ) <= TolVar: + break + #end + #end + #fmean = [fx,fmean]; + fmean = numpy.hstack((fx,fmean)); + if (len(fmean) > 10): + fmean = fmean[:10] + + + + #--- Updating zk + + apk = 0.5*(k+1); + Ak = Ak + apk; + tauk = 2/(k+3); + + wk = apk*df + wk; + + # + # zk = Argmin_x Lmu/2 ||b - Ax||_l2^2 + Lmu/2||x - xplug ||_2^2+ <wk,x-xk> + # s.t. ||b-Ax||_l2 < delta + # + + cp = xplug - 1.0/Lmu*wk; + + Acp = Afun( cp ); + #if ~isempty( AAtinv ) && isempty(USV) + if AAtinv is not None and USV is None: + AtAcp = Atfun( AAtinv_fun( Acp ) ); + else: + AtAcp = Atfun( Acp ); + #end + + if delta > 0: + #if ~isempty(USV) + if USV is not None: + # Make the substitution wk <-- wk/K + + # dfp = (xplug - Lmu1*wk); # = cp + # Adfp= (Axplug - Acp); + dfp = cp.copy() + Adfp = Acp.copy() + bp = b.copy(); + deltap = delta; + # dfp = SLmu*xplug - SLmu1*wk; + # bp = SLmu*b; + # deltap = SLmu*delta; + + # See if we even need to project: + if numpy.linalg.norm( Adfp - bp ) < deltap: + zk = dfp.copy(); + Azk = Adfp.copy(); + else: + projection,projIter,lambdaZ = fastProjection(Q,S,V,dfp,bp,deltap, .999*lambdaZ ) + if lambdaZ > 0: + print 'lambda is positive!' + raise + zk = projection.copy(); + # zk = SLmu1*projection; + Azk = Afun(zk); + + # DEBUGGING: + # if projIter == 50 + # fprintf('\n Maxed out iterations at z\n'); + # keyboard + # end + #end + else: + lambdaa = max(0,Lmu*(numpy.linalg.norm(b-Acp)/delta - 1)); + gamma = lambdaa/(lambdaa + Lmu); + zk = lambdaa/Lmu*(1-gamma)*Atb + cp - gamma*AtAcp; + # for calculating the residual, we'll avoid calling A() + # by storing A(zk) here (using A'*A = I): + Azk = lambdaa/Lmu*(1-gamma)*b + Acp - gamma*Acp; + #end + else: + # if delta is 0, this is simplified: + zk = AtAAtb + cp - AtAcp; + Azk = b; + #end + + # DEBUGGING + # if norm( Ayk - b ) > (1.05)*delta + # fprintf('\nAzk failed projection test\n'); + # keyboard; + # end + + #--- Updating xk + + xkp = tauk*zk + (1-tauk)*yk; + xold = xk.copy(); + xk = xkp.copy(); + Axk = tauk*Azk + (1-tauk)*Ayk; + + #if ~mod(k,10), Axk = Afun(xk); end # otherwise slowly lose precision + if not numpy.mod(k,10): + Axk = Afun(xk) + # DEBUG + # if norm(Axk - Afun(xk) ) > 1e-6, disp('error with Axk'); keyboard; end + + #--- display progress if desired + #if ~mod(k+1,Verbose ) + if not numpy.mod(k+1,Verbose): + #printf('Iter: #3d ~ fmu: #.3e ~ Rel. Variation of fmu: #.2e ~ Residual: #.2e',k+1,fx,qp,residuals(k+1,1) ); + print 'Iter: ',k+1,' ~ fmu: ',fx,' ~ Rel. Variation of fmu: ',qp,' ~ Residual:',residuals[k+1,0] + #--- if user has supplied a function to calculate the error, + # apply it to the current iterate and dislay the output: + #if DISPLAY_ERROR, printf(' ~ Error: #.2e',errFcn(xk)); end + if DISPLAY_ERROR: + print ' ~ Error:',errFcn(xk) + #end + if abs(fx)>1e20 or abs(residuals[k,0]) >1e20 or numpy.isnan(fx): + #error('Nesta: possible divergence or NaN. Bad estimate of ||A''A||?'); + print 'Nesta: possible divergence or NaN. Bad estimate of ||A''A||?' + raise + #end + + #end + + niter = k+1; + + #-- truncate output vectors + residuals = residuals[:niter,:] + #if RECORD_DATA, outputData = outputData(1:niter,:); end + if RECORD_DATA: + outputData = outputData[:niter,:] + + return xk,niter,residuals,outputData,opts + + +############ PERFORM THE L1 CONSTRAINT ################## + +#function [df,fx,val,uk] = Perform_L1_Constraint(xk,mu,U,Ut) +def Perform_L1_Constraint(xk,mu,U,Ut): + + #if isa(U,'function_handle') + if hasattr(U,'__call__'): + uk = U(xk); + else: + uk = numpy.dot(U,xk) + #end + fx = uk.copy() + + #uk = uk./max(mu,abs(uk)); + uk = uk / max(mu,abs(uk)) + #val = real(uk'*fx); + val = numpy.real(numpy.vdot(uk,fx)) + #fx = real(uk'*fx - mu/2*norm(uk)^2); + fx = numpy.real(numpy.vdot(uk,fx) - mu/2.*numpy.linalg.norm(uk)**2); + + #if isa(Ut,'function_handle') + if hasattr(U,'__call__'): + df = Ut(uk); + else: + #df = U'*uk; + df = numpy.dot(U.T,uk) + #end + return df,fx,val,uk +#end + +# Nic: TODO: TV not implemented yet! +############ PERFORM THE TV CONSTRAINT ################## +#function [df,fx] = Perform_TV_Constraint(xk,mu,Dv,Dh,D,U,Ut) +# if isa(U,'function_handle') +# x = U(xk); +# else +# x = U*xk; +# end +# df = zeros(size(x)); +# +# Dhx = Dh*x; +# Dvx = Dv*x; +# +# tvx = sum(sqrt(abs(Dhx).^2+abs(Dvx).^2)); +# w = max(mu,sqrt(abs(Dhx).^2 + abs(Dvx).^2)); +# uh = Dhx ./ w; +# uv = Dvx ./ w; +# u = [uh;uv]; +# fx = real(u'*D*x - mu/2 * 1/numel(u)*sum(u'*u)); +# if isa(Ut,'function_handle') +# df = Ut(D'*u); +# else +# df = U'*(D'*u); +# end +#end + + +#function [x,k,l] = fastProjection( U, S, V, y, b, epsilon, lambda0, DISP ) +def fastProjection( U, S, V, y, b, epsilon, lambda0=0, DISP=False ): + # [x,niter,lambda] = fastProjection(U, S, V, y, b, epsilon, [lambda0], [DISP] ) + # + # minimizes || x - y || + # such that || Ax - b || <= epsilon + # + # where USV' = A (i.e the SVD of A) + # + # The optional input "lambda0" is a guess for the Lagrange parameter + # + # Warning: for speed, does not calculate A(y) to see if x = y is feasible + # + # NESTA Version 1.1 + # See also Core_Nesterov + + # Written by Stephen Becker, September 2009, srbecker@caltech.edu + + #--------------------- + # Original Matab code: + + #DEBUG = true; + #if nargin < 8 + # DISP = false; + #end + ## -- Parameters for Newton's method -- + #MAXIT = 70; + #TOL = 1e-8 * epsilon; + ## TOL = max( TOL, 10*eps ); # we can't do better than machine precision + # + #m = size(U,1); + #n = size(V,1); + #mn = min([m,n]); + #if numel(S) > mn^2, S = diag(diag(S)); end # S should be a small square matrix + #r = size(S); + #if size(U,2) > r, U = U(:,1:r); end + #if size(V,2) > r, V = V(:,1:r); end + # + #s = diag(S); + #s2 = s.^2; + # + ## What we want to do: + ## b = b - A*y; + ## bb = U'*b; + # + ## if A doesn't have full row rank, then b may not be in the range + #if size(U,1) > size(U,2) + # bRange = U*(U'*b); + # bNull = b - bRange; + # epsilon = sqrt( epsilon^2 - norm(bNull)^2 ); + #end + #b = U'*b - S*(V'*y); # parenthesis is very important! This is expensive. + # + ## b2 = b.^2; + #b2 = abs(b).^2; # for complex data + #bs2 = b2.*s2; + #epsilon2 = epsilon^2; + # + ## The following routine need to be fast + ## For efficiency (at cost of transparency), we are writing the calculations + ## in a way that minimize number of operations. The functions "f" + ## and "fp" represent f and its derivative. + # + ## f = @(lambda) sum( b2 .*(1-lambda*s2).^(-2) ) - epsilon^2; + ## fp = @(lambda) 2*sum( bs2 .*(1-lambda*s2).^(-3) ); + #if nargin < 7, lambda0 = 0; end + #l = lambda0; oldff = 0; + #one = ones(m,1); + #alpha = 1; # take full Newton steps + #for k = 1:MAXIT + # # make f(l) and fp(l) as efficient as possible: + # ls = one./(one-l*s2); + # ls2 = ls.^2; + # ls3 = ls2.*ls; + # ff = b2.'*ls2; # should be .', not ', even for complex data + # ff = ff - epsilon2; + # fpl = 2*( bs2.'*ls3 ); # should be .', not ', even for complex data + ## ff = f(l); # this is a little slower + ## fpl = fp(l); # this is a little slower + # d = -ff/fpl; + # if DISP, fprintf('#2d, lambda is #5.2f, f(lambda) is #.2e, f''(lambda) is #.2e\n',... + # k,l,ff,fpl ); end + # if abs(ff) < TOL, break; end # stopping criteria + # l_old = l; + # if k>2 && ( abs(ff) > 10*abs(oldff+100) ) #|| abs(d) > 1e13 ) + # l = 0; alpha = 1/2; + ## oldff = f(0); + # oldff = sum(b2); oldff = oldff - epsilon2; + # if DISP, disp('restarting'); end + # else + # if alpha < 1, alpha = (alpha+1)/2; end + # l = l + alpha*d; + # oldff = ff; + # if l > 0 + # l = 0; # shouldn't be positive + # oldff = sum(b2); oldff = oldff - epsilon2; + # end + # end + # if l_old == l && l == 0 + # if DISP, disp('Making no progress; x = y is probably feasible'); end + # break; + # end + #end + ## if k == MAXIT && DEBUG, disp('maxed out iterations'); end + #if l < 0 + # xhat = -l*s.*b./( 1 - l*s2 ); + # x = V*xhat + y; + #else + # # y is already feasible, so no need to project + # l = 0; + # x = y; + #end + + # End of original Matab code + #--------------------- + + DEBUG = True; + #if nargin < 8 + # DISP = false; + #end + # -- Parameters for Newton's method -- + MAXIT = 70; + TOL = 1e-8 * epsilon; + # TOL = max( TOL, 10*eps ); # we can't do better than machine precision + + #m = size(U,1); + #n = size(V,1); + m = U.shape[0] + n = V.shape[0] + mn = min(m,n); + #if numel(S) > mn^2, S = diag(diag(S)); end # S should be a small square matrix + if S.size > mn**2: + S = numpy.diag(numpy.diag(S)) + #r = size(S); + r = S.shape + #if size(U,2) > r, U = U(:,1:r); end + if U.shape[1] > r: + U = U[:,r] + #if size(V,2) > r, V = V(:,1:r); end + if V.shape[1] > r: + V = V[:,r] + + s = numpy.diag(S); + s2 = s**2; + + # What we want to do: + # b = b - A*y; + # bb = U'*b; + + # if A doesn't have full row rank, then b may not be in the range + #if size(U,1) > size(U,2) + if U.shape[0] > U.shape[1]: + #bRange = U*(U'*b); + bRange = numpy.dot(U, numpy.dot(U.T, b)) + bNull = b - bRange; + epsilon = math.sqrt( epsilon**2 - numpy.linalg.norm(bNull)**2 ); + #end + #b = U'*b - S*(V'*y); # parenthesis is very important! This is expensive. + b = numpy.dot(U.T,b) - numpy.dot(S, numpy.dot(V.T,y)) + + # b2 = b.^2; + b2 = abs(b)**2; # for complex data + bs2 = b2**s2; + epsilon2 = epsilon**2; + + # The following routine need to be fast + # For efficiency (at cost of transparency), we are writing the calculations + # in a way that minimize number of operations. The functions "f" + # and "fp" represent f and its derivative. + + # f = @(lambda) sum( b2 .*(1-lambda*s2).^(-2) ) - epsilon^2; + # fp = @(lambda) 2*sum( bs2 .*(1-lambda*s2).^(-3) ); + + #if nargin < 7, lambda0 = 0; end + l = lambda0; + oldff = 0; + one = numpy.ones(m); + alpha = 1; # take full Newton steps + #for k = 1:MAXIT + for k in numpy.arange(MAXIT): + # make f(l) and fp(l) as efficient as possible: + #ls = one./(one-l*s2); + ls = one/(one-l*s2) + ls2 = ls**2; + ls3 = ls2**ls; + #ff = b2.'*ls2; # should be .', not ', even for complex data + ff = numpy.dot(b2.conj(), ls2) + ff = ff - epsilon2; + #fpl = 2*( bs2.'*ls3 ); # should be .', not ', even for complex data + fpl = 2 * numpy.dot(bs2.conj(),ls3) + # ff = f(l); # this is a little slower + # fpl = fp(l); # this is a little slower + d = -ff/fpl; + # if DISP, fprintf('#2d, lambda is #5.2f, f(lambda) is #.2e, f''(lambda) is #.2e\n',k,l,ff,fpl ); end + if DISP: + print k,', lambda is ',l,', f(lambda) is ',ff,', f''(lambda) is',fpl + #if abs(ff) < TOL, break; end # stopping criteria + if abs(ff) < TOL: + break + l_old = l.copy(); + if k>2 and ( abs(ff) > 10*abs(oldff+100) ): #|| abs(d) > 1e13 ) + l = 0; + alpha = 1.0/2.0; + # oldff = f(0); + oldff = b2.sum(); oldff = oldff - epsilon2; + if DISP: + print 'restarting' + else: + if alpha < 1: + alpha = (alpha+1.0)/2.0 + l = l + alpha*d; + oldff = ff + if l > 0: + l = 0; # shouldn't be positive + oldff = b2.sum() + oldff = oldff - epsilon2; + #end + #end + if l_old == l and l == 0: + #if DISP, disp('Making no progress; x = y is probably feasible'); end + if DISP: + print 'Making no progress; x = y is probably feasible' + break; + #end + #end + # if k == MAXIT && DEBUG, disp('maxed out iterations'); end + if l < 0: + #xhat = -l*s.*b./( 1 - l*s2 ); + xhat = numpy.dot(-l, s*b/( 1. - numpy.dot(l,s2) ) ) + #x = V*xhat + y; + x = numpy.dot(V,xhat) + y + else: + # y is already feasible, so no need to project + l = 0; + x = y.copy(); + #end + return x,k,l \ No newline at end of file