idamnjanovic@8: function solver = SMALL_solve(Problem, solver) ivan@128: %% SMALL sparse solver idamnjanovic@24: % ivan@85: % Function gets as input SMALL structure that contains SPARCO problem to ivan@85: % be solved, name of the toolbox and solver, and parameters file for ivan@85: % particular solver. ivan@85: % ivan@85: % Outputs are solution, reconstructed signal and time spent ivan@85: idamnjanovic@24: % Centre for Digital Music, Queen Mary, University of London. idamnjanovic@24: % This file copyright 2009 Ivan Damnjanovic. idamnjanovic@24: % idamnjanovic@24: % This program is free software; you can redistribute it and/or idamnjanovic@24: % modify it under the terms of the GNU General Public License as idamnjanovic@24: % published by the Free Software Foundation; either version 2 of the idamnjanovic@24: % License, or (at your option) any later version. See the file idamnjanovic@24: % COPYING included with this distribution for more information. danieleb@156: % idamnjanovic@8: %% idamnjanovic@8: idamnjanovic@8: if isa(Problem.A,'float') idamnjanovic@8: A = Problem.A; idamnjanovic@8: SparseLab_A=Problem.A; idamnjanovic@8: m = size(Problem.A,1); % m is the no. of rows. idamnjanovic@8: n = size(Problem.A,2); % n is the no. of columns. idamnjanovic@8: else idamnjanovic@8: A = @(x) Problem.A(x,1); % The operator idamnjanovic@8: AT = @(y) Problem.A(y,2); % and its transpose. idamnjanovic@8: SparseLab_A =@(mode, m, n, x, I, dim) SL_A(Problem.A, mode, m, n, x, I, dim); idamnjanovic@8: m = Problem.sizeA(1); % m is the no. of rows. idamnjanovic@8: n = Problem.sizeA(2); % n is the no. of columns. idamnjanovic@8: idamnjanovic@1: end idamnjanovic@37: % if signal that needs to be represented is different then training set for idamnjanovic@37: % dictionary learning it should be stored in Problem.b1 matix idamnjanovic@37: if isfield(Problem, 'b1') idamnjanovic@37: b = Problem.b1; idamnjanovic@37: else idamnjanovic@37: b = Problem.b; % The right-hand-side vector. idamnjanovic@37: end idamnjanovic@8: %% ivan@140: if (solver.profile) ivan@140: fprintf('\nStarting solver %s... \n', solver.name); ivan@140: end ivan@140: idamnjanovic@8: start=cputime; idamnjanovic@37: tStart=tic; danieleb@156: switch solver.toolbox danieleb@156: case 'sparselab' danieleb@156: y = eval([solver.name,'(SparseLab_A, b, n,',solver.param,');']); danieleb@156: case 'sparsify' danieleb@156: y = eval([solver.name,'(b, A, n, ''P_trans'', AT,',solver.param,');']); danieleb@156: case 'spgl1' danieleb@156: y = eval([solver.name,'(b, A,',solver.param,');']); danieleb@156: case 'gpsr' danieleb@156: y = eval([solver.name,'(b, A,',solver.param,');']); danieleb@156: case 'SPAMS' danieleb@156: y = eval([solver.name,'(b, A, solver.param);']); danieleb@156: case 'SMALL' danieleb@156: if isa(Problem.A,'float') danieleb@156: y = eval([solver.name,'(A, b, n,',solver.param,');']); danieleb@156: else danieleb@156: y = eval([solver.name,'(A, b, n,',solver.param,',AT);']); danieleb@156: end danieleb@156: case 'ompbox' danieleb@156: G = A'*A; danieleb@156: maxatoms=solver.param.maxatoms; danieleb@156: switch solver.name danieleb@156: case 'omp' danieleb@156: y = omp(A,b,G,maxatoms,'checkdict','off'); danieleb@156: case 'omp2' danieleb@156: epsilon=solver.param.epsilon; danieleb@156: y = omp2(A,b,G,epsilon,'maxatoms',maxatoms,'checkdict','off'); danieleb@156: end danieleb@156: case 'ompsbox' danieleb@156: basedict = Problem.basedict; danieleb@156: if issparse(Problem.A) danieleb@156: A = Problem.A; danieleb@156: else danieleb@156: A = sparse(Problem.A); danieleb@156: end danieleb@156: G = dicttsep(basedict,A,dictsep(basedict,A,speye(size(A,2)))); danieleb@156: epsilon=solver.param.epsilon; danieleb@156: maxatoms=solver.param.maxatoms; danieleb@156: y = eval([solver.name,'(basedict, A, b, G,epsilon,''maxatoms'',maxatoms,''checkdict'',''off'');']); danieleb@156: Problem.sparse=1; danieleb@156: % To introduce new sparse representation algorithm put the files in danieleb@156: % your Matlab path. Next, unique name for your toolbox and danieleb@156: % prefferd API needs to be defined. danieleb@156: % danieleb@156: % elseif strcmpi(solver.toolbox,'') danieleb@156: % danieleb@156: % % - Evaluate the function (solver.name - defined in the main) with danieleb@156: % % parameters given above danieleb@156: % danieleb@156: % y = eval([solver.name,'();']); danieleb@156: otherwise danieleb@156: printf('\nToolbox has not been registered. Please change SMALL_solver file.\n'); danieleb@156: return idamnjanovic@8: end idamnjanovic@8: idamnjanovic@8: %% idamnjanovic@8: % Sparse representation time idamnjanovic@37: tElapsed=toc(tStart); idamnjanovic@8: solver.time = cputime - start; ivan@140: if (solver.profile) ivan@140: fprintf('Solver %s finished task in %2f seconds (cpu time). \n', solver.name, solver.time); ivan@140: fprintf('Solver %s finished task in %2f seconds (tic-toc time). \n', solver.name, tElapsed); ivan@140: end idamnjanovic@37: solver.time=tElapsed; idamnjanovic@8: % geting around out of memory problem when converting big matrix from idamnjanovic@8: % sparse to full... idamnjanovic@8: idamnjanovic@18: if isfield(Problem, 'sparse')&&(Problem.sparse==1) idamnjanovic@8: solver.solution = y; idamnjanovic@8: else idamnjanovic@8: solver.solution = full(y); idamnjanovic@8: end idamnjanovic@37: if isfield(Problem,'reconstruct') ivan@140: % Reconstruct the signal from the solution ivan@140: solver.reconstructed = Problem.reconstruct(solver.solution); idamnjanovic@8: end idamnjanovic@37: end