sparsify/private/greed_omp_cg.m

function [s, err_mse, iter_time]=greed_omp_cg(x,A,m,varargin)
%-*- texinfo -*-
%@deftypefn {Function} greed_omp_cg
%@verbatim
% greed_omp_cg: Orthogonal Matching Pursuit algorithm based on full
% conjugate gradient solver
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Usage
% [s, err_mse, iter_time]=greed_omp_cg(x,P,m,'option_name','option_value')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Input
%   Mandatory:
%               x   Observation vector to be decomposed
%               P   Either:
%                       1) An nxm matrix (n must be dimension of x)
%                       2) A function handle (type "help function_format"
%                          for more information)
%                          Also requires specification of P_trans option.
%                       3) An object handle (type "help object_format" for
%                          more information)
%               m   length of s
%
%   Possible additional options:
%   (specify as many as you want using 'option_name','option_value' pairs)
%   See below for explanation of options:
%__________________________________________________________________________
%   option_name         AVAILABLE OPTION_VALUES                 default
%--------------------------------------------------------------------------
%   stopCrit        M, CORR, MSE, MSE_CHANGE                    M
%   stopTol         NUMBER (SEE BELOW)                          n/4
%   cgTol           NUMBER (SEE BELOW)                          1e-6
%   P_trans         FUNCTION_HANDLE (SEE BELOW)
%   maxIter         POSITIVE INTEGER (SEE BELOW)                n
%   verbose         TRUE, FALSE                                 false
%   start_val       VECTOR OF LENGTH M                          zeros
%
%   Available stopping criteria :
%               M           -   Extracts exactly M = stopTol elements.
%               corr        -   Stops when maximum correlation between
%                               residual and atoms is below stopTol value.
%               mse         -   Stops when mean squared error of residual
%                               is below stopTol value.
%               mse_change  -   Stops when the change in the mean squared
%                               error falls below stopTol value.
%
%   stopTol: Value for stopping criterion.
%
%   cgTol:   Value for conjugate gradient stoping criterion.
%
%   P_trans: If P is a function handle, then P_trans has to be specified and
%            must be a function handle.
%
%   maxIter: Maximum of allowed iterations.
%
%   verbose: Logical value to allow algorithm progress to be displayed.
%
%   start_val: Allows algorithms to start from partial solution.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Outputs
%    s              Solution vector
%    err_mse        Vector containing mse of approximation error for each
%                   iteration
%    iter_time      Vector containing times for each iteration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Description
%   greed_omp_cg performs a greedy signal decomposition.
%   In each iteration a new element is selected depending on the inner
%   product between the current residual and columns in P.
%   The non-zero elements of s are approximated by orthogonally projecting
%   x onto the selected elements in each iteration.
%   The inverse problem required for orthogonal matching pursuit is solved
%   using a conjugate gradient algorithm in each iteration. This is slow but
%   does not require any additional storage and can therefore be used for
%   large scale problems.
%
% See Also
%   greed_qr, greed_omp_chol, greed_omp_cgp, greed_omp_pinv,
%   greed_omp_linsolve, greed_gp, greed_nomp
%
% Copyright (c) 2007 Thomas Blumensath
%
% The University of Edinburgh
% Email: thomas.blumensath@ed.ac.uk
% Comments and bug reports welcome
%
% This file is part of sparsity Version 0.1
% Created: April 2007
%
% Part of this toolbox was developed with the support of EPSRC Grant
% D000246/1
%
% Please read COPYRIGHT.m for terms and conditions.
%@end verbatim
%@strong{Url}: @url{http://ltfat.github.io/doc/thirdparty/sparsify/private/greed_omp_cg.html}
%@end deftypefn

% Copyright (C) 2005-2016 Peter L. Soendergaard <peter@sonderport.dk>.
% This file is part of LTFAT version 2.3.1
%
% 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 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program.  If not, see <http://www.gnu.org/licenses/>.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                    Default values and initialisation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


[n1 n2]=size(x);
if n2 == 1
    n=n1;
elseif n1 == 1
    x=x';
    n=n2;
else
   display('x must be a vector.');
   return
end

sigsize     = x'*x/n;
initial_given=0;
err_mse     = [];
iter_time   = [];
STOPCRIT    = 'M';
STOPTOL     = ceil(n/4);
MAXITER     = n;
verbose     = false;
s_initial   = zeros(m,1);
cg_tol      = 1e-6;
vectnfact   = ones(m,1);

if verbose
   display('Initialising...')
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                           Output variables
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

switch nargout
    case 3
        comp_err=true;
        comp_time=true;
    case 2
        comp_err=true;
        comp_time=false;
    case 1
        comp_err=false;
        comp_time=false;
    case 0
        error('Please assign output variable.')
    otherwise
        error('Too many output arguments specified')
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                       Look through options
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Put option into nice format
Options={};
OS=nargin-3;
c=1;
for i=1:OS
    if isa(varargin{i},'cell')
        CellSize=length(varargin{i});
        ThisCell=varargin{i};
        for j=1:CellSize
            Options{c}=ThisCell{j};
            c=c+1;
        end
    else
        Options{c}=varargin{i};
        c=c+1;
    end
end
OS=length(Options);
if rem(OS,2)
   error('Something is wrong with argument name and argument value pairs.')
end

for i=1:2:OS
   switch Options{i}
        case {'stopCrit'}
            if (strmatch(Options{i+1},{'M'; 'corr'; 'mse'; 'mse_change'},'exact'));
                STOPCRIT    = Options{i+1};
            else error('stopCrit must be char string [M, corr, mse, mse_change]. Exiting.'); end
        case {'stopTol'}
            if isa(Options{i+1},'numeric') ; STOPTOL     = Options{i+1};
            else error('stopTol must be number. Exiting.'); end
        case {'cgTol'}
            if isa(Options{i+1},'numeric') ; cg_tol     = Options{i+1};
            else error('cgTol must be number. Exiting.'); end
        case {'P_trans'}
            if isa(Options{i+1},'function_handle'); Pt = Options{i+1};
            else error('P_trans must be function _handle. Exiting.'); end
        case {'maxIter'}
            if isa(Options{i+1},'numeric'); MAXITER     = Options{i+1};
            else error('maxIter must be a number. Exiting.'); end
        case {'verbose'}
            if isa(Options{i+1},'logical'); verbose     = Options{i+1};
            else error('verbose must be a logical. Exiting.'); end
        case {'vecNormFac'}
            if isa(Options{i+1},'numeric')& length(Options{i+1}) == m , vectnfact = Options{i+1};
            else error('verbose must be a logical. Exiting.'); end
        case {'start_val'}
            if isa(Options{i+1},'numeric') & length(Options{i+1}) == m ;
                s_initial     = Options{i+1};
                initial_given=1;
            else error('start_val must be a vector of length m. Exiting.'); end
        otherwise
            error('Unrecognised option. Exiting.')
   end
end


if strcmp(STOPCRIT,'M')
    maxM=STOPTOL;
else
    maxM=MAXITER;
end

if nargout >=2
    err_mse = zeros(maxM,1);
end
if nargout ==3
    iter_time = zeros(maxM,1);
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                        Make P and Pt functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

if          isa(A,'float')      P =@(z) A*z;  Pt =@(z) A'*z;
elseif      isobject(A)         P =@(z) A*z;  Pt =@(z) A'*z;
elseif      isa(A,'function_handle')
    try
        if          isa(Pt,'function_handle'); P=A;
        else        error('If P is a function handle, Pt also needs to be a function handle. Exiting.'); end
    catch error('If P is a function handle, Pt needs to be specified. Exiting.'); end
else        error('P is of unsupported type. Use matrix, function_handle or object. Exiting.'); end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                        Do we start from zero or not?
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

if initial_given ==1;
    IN          = find(s_initial);
    Residual    = x-P(s_initial);
    s           = s_initial;
    oldERR      = Residual'*Residual/n;

else
    IN          = [];
    Residual    = x;
    s           = s_initial;
    sigsize     = x'*x/n;
    oldERR      = sigsize;
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                 Random Check to see if dictionary is normalised
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%         mask=zeros(m,1);
%         mask(ceil(rand*m))=1;
%         nP=norm(P(mask));
%         if abs(1-nP)>1e-3;
%             display('Dictionary appears not to have unit norm columns.')
%         end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                        Main algorithm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if verbose
   display('Main iterations...')
end
tic
t=0;
DR=Pt(Residual).*vectnfact;
done = 0;
iter=1;
while ~done
     DR(IN)=0;
     [v I]=max(abs(DR));
     IN=[IN I];
       %      abs(DR(IN))
       % pause
     [s Residual]=SubsetCG(x,s,P,Pt,IN,cg_tol,verbose);
     DR=Pt(Residual).*vectnfact;


     ERR=Residual'*Residual/n;
     if comp_err
         err_mse(iter)=ERR;
     end

     if comp_time
         iter_time(iter)=toc;
     end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                        Are we done yet?
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     if strcmp(STOPCRIT,'M')
         if iter >= STOPTOL
             done =1;
         elseif verbose && toc-t>10
            display(sprintf('Iteration %i. --- %i iterations to go',iter ,STOPTOL-iter))
            t=toc;
         end
    elseif strcmp(STOPCRIT,'mse')
         if comp_err
            if err_mse(iter)<STOPTOL;
                done = 1;
            elseif verbose && toc-t>10
                display(sprintf('Iteration %i. --- %i mse',iter ,err_mse(iter)))
                t=toc;
            end
         else
             if ERR<STOPTOL;
                done = 1;
             elseif verbose && toc-t>10
                display(sprintf('Iteration %i. --- %i mse',iter ,ERR))
                t=toc;
             end
         end
     elseif strcmp(STOPCRIT,'mse_change') && iter >=2
         if comp_err && iter >=2
              if ((err_mse(iter-1)-err_mse(iter))/sigsize <STOPTOL);
                done = 1;
             elseif verbose && toc-t>10
                display(sprintf('Iteration %i. --- %i mse change',iter ,(err_mse(iter-1)-err_mse(iter))/sigsize ))
                t=toc;
             end
         else
             if ((oldERR - ERR)/sigsize < STOPTOL);
                done = 1;
             elseif verbose && toc-t>10
                display(sprintf('Iteration %i. --- %i mse change',iter ,(oldERR - ERR)/sigsize))
                t=toc;
             end
         end
     elseif strcmp(STOPCRIT,'corr')
          if max(abs(DR)) < STOPTOL;
             done = 1;
          elseif verbose && toc-t>10
                display(sprintf('Iteration %i. --- %i corr',iter ,max(abs(DR))))
                t=toc;
          end
     end

    % Also stop if residual gets too small or maxIter reached
     if comp_err
         if err_mse(iter)<1e-16
             display('Stopping. Exact signal representation found!')
             done=1;
         end
     else


         if iter>1
             if ERR<1e-16
                 display('Stopping. Exact signal representation found!')
                 done=1;
             end
         end
     end

     if iter >= MAXITER
         display('Stopping. Maximum number of iterations reached!')
         done = 1;
     end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                    If not done, take another round
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

     if ~done
        iter=iter+1;
        oldERR=ERR;
     end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                  Only return as many elements as iterations
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

if nargout >=2
    err_mse = err_mse(1:iter);
end
if nargout ==3
    iter_time = iter_time(1:iter);
end

if verbose
   display('Done')
end

% Change history
%
% 8 of Februray: Algo does no longer stop if dictionary is not normaliesd.