Mercurial > hg > smallbox
view util/SMALL_solve.m @ 166:1495bdfa13e9 danieleb
Updated grassmanian function (restored old computation of the dictionary) and added functions to the audio class
author | Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk> |
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date | Mon, 19 Sep 2011 14:53:23 +0100 |
parents | 23763c5fbda5 |
children | 4ea4badb2266 |
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function solver = SMALL_solve(Problem, solver) %% SMALL sparse solver % % Function gets as input SMALL structure that contains SPARCO problem to % be solved, name of the toolbox and solver, and parameters file for % particular solver. % % Outputs are solution, reconstructed signal and time spent % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2009 Ivan Damnjanovic. % % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License as % published by the Free Software Foundation; either version 2 of the % License, or (at your option) any later version. See the file % COPYING included with this distribution for more information. % %% if isa(Problem.A,'float') A = Problem.A; SparseLab_A=Problem.A; m = size(Problem.A,1); % m is the no. of rows. n = size(Problem.A,2); % n is the no. of columns. else A = @(x) Problem.A(x,1); % The operator AT = @(y) Problem.A(y,2); % and its transpose. SparseLab_A =@(mode, m, n, x, I, dim) SL_A(Problem.A, mode, m, n, x, I, dim); m = Problem.sizeA(1); % m is the no. of rows. n = Problem.sizeA(2); % n is the no. of columns. end % if signal that needs to be represented is different then training set for % dictionary learning it should be stored in Problem.b1 matix if isfield(Problem, 'b1') b = Problem.b1; else b = Problem.b; % The right-hand-side vector. end %% if (solver.profile) fprintf('\nStarting solver %s... \n', solver.name); end start=cputime; tStart=tic; if strcmpi(solver.toolbox,'sparselab') y = eval([solver.name,'(SparseLab_A, b, n,',solver.param,');']); elseif strcmpi(solver.toolbox,'sparsify') if isa(Problem.A,'float') y = eval([solver.name,'(b, A, n,',solver.param,');']); else y = eval([solver.name,'(b, A, n, ''P_trans'', AT,',solver.param,');']); end elseif (strcmpi(solver.toolbox,'spgl1')||strcmpi(solver.toolbox,'gpsr')) y = eval([solver.name,'(b, A,',solver.param,');']); elseif (strcmpi(solver.toolbox,'SPAMS')) y = eval([solver.name,'(b, A, solver.param);']); elseif (strcmpi(solver.toolbox,'SMALL')) if isa(Problem.A,'float') y = eval([solver.name,'(A, b, n,',solver.param,');']); else y = eval([solver.name,'(A, b, n,',solver.param,',AT);']); end elseif (strcmpi(solver.toolbox, 'ompbox')) G=A'*A; epsilon=solver.param.epsilon; maxatoms=solver.param.maxatoms; y = eval([solver.name,'(A, b, G,epsilon,''maxatoms'',maxatoms,''checkdict'',''off'');']); elseif (strcmpi(solver.toolbox, 'ompsbox')) basedict = Problem.basedict; if issparse(Problem.A) A = Problem.A; else A = sparse(Problem.A); end G = dicttsep(basedict,A,dictsep(basedict,A,speye(size(A,2)))); epsilon=solver.param.epsilon; maxatoms=solver.param.maxatoms; y = eval([solver.name,'(basedict, A, b, G,epsilon,''maxatoms'',maxatoms,''checkdict'',''off'');']); Problem.sparse=1; elseif (strcmpi(solver.toolbox, 'ALPS')) if ~isa(Problem.A,'float') % ALPS does not accept implicit dictionary definition A = opToMatrix(Problem.A, 1); end [y, numiter, time, y_path] = wrapper_ALPS_toolbox(b, A, solver.param); elseif (strcmpi(solver.toolbox, 'MMbox')) if ~isa(Problem.A,'float') % ALPS does not accept implicit dictionary definition A = opToMatrix(Problem.A, 1); end [y, cost] = wrapper_mm_solver(b, A, solver.param); % To introduce new sparse representation algorithm put the files in % your Matlab path. Next, unique name <TolboxID> for your toolbox and % prefferd API <Preffered_API> needs to be defined. % % elseif strcmpi(solver.toolbox,'<ToolboxID>') % % % - Evaluate the function (solver.name - defined in the main) with % % parameters given above % % y = eval([solver.name,'(<Preffered_API>);']); else printf('\nToolbox has not been registered. Please change SMALL_solver file.\n'); return end %% % Sparse representation time tElapsed=toc(tStart); solver.time = cputime - start; if (solver.profile) fprintf('Solver %s finished task in %2f seconds (cpu time). \n', solver.name, solver.time); fprintf('Solver %s finished task in %2f seconds (tic-toc time). \n', solver.name, tElapsed); end solver.time=tElapsed; % geting around out of memory problem when converting big matrix from % sparse to full... if isfield(Problem, 'sparse')&&(Problem.sparse==1) solver.solution = y; else solver.solution = full(y); end if isfield(Problem,'reconstruct') % Reconstruct the signal from the solution solver.reconstructed = Problem.reconstruct(solver.solution); end end