source: proiecte/pmake3d/make3d_original/Make3dSingleImageStanford_version0.1/third_party/opt/yalmip/extras/matrixcoefficients.m @ 37

function [base,v] = matrixcoefficients(p,x)
%MATRIXCOEFFICIENTS Extends coefficients, beta

% Author Johan Löfberg
% $Id: matrixcoefficients.m,v 1.4 2006/08/09 12:14:04 joloef Exp $


% FIX: CURRENTLY UNSTABLE!

if nargout>1 & (max(size(p))>1)
    % error('For matrix inputs, only the coefficients can be returned. Request feature if you need this...');
end

% Hack to make sure we get the basis w.r.t all variables and 1
% This has to be fixed soon (to make robust opt. module fast)
p = p + pi + sum(x)*1e-5;

if nargin==1
    allvar = depends(p);
    xvar = allvar;
    x = recover(xvar);
else
    xvar = intersect(depends(x),depends(p));
end

% Try to debug this!
p = p(:);
base = [];
v = [];
allvar = depends(p);%(ii));
allvar_recovered = recover(allvar);
t = setdiff(allvar,xvar);
t_recovered = recover(t);
ParametricIndicies = find(ismember(allvar,t));
map = find(~ismember(allvar,t));

for ii = 1:length(p)
    pii = p(ii);
    [exponent_p,p_base] = getexponentbase(pii,allvar_recovered);

    tempbase = parameterizedbase(pii,[],t_recovered,ParametricIndicies,exponent_p,p_base);
    [i,j,k] = unique(full(exponent_p(:,map)),'rows');


    V = sparse(1:length(k),k,1,length(tempbase),max(k))';

    base{ii} = V*tempbase - [pi;repmat(1e-5,length(x),1)];

    keepthese = j(1:max(k));
    v{ii} = recovermonoms(exponent_p(keepthese,map),x);%recover(xvar));
end






function p_base_parametric = parameterizedbase(p,z, params,ParametricIndicies,exponent_p,p_base)

% Check for linear parameterization
parametric_basis = exponent_p(:,ParametricIndicies);
if all(sum(parametric_basis,2)==0)
    p_base_parametric = full(p_base(:));
    return
end
if all(sum(parametric_basis,2)<=1)
    p_base_parametric = full(p_base(:));
    n = length(p_base_parametric);
    ii = [];
    vars = [];
    js = sum(parametric_basis,1);
    for i = 1:size(parametric_basis,2)
        if js(i)
            j = find(parametric_basis(:,i));
            ii = [ii j(:)'];
            vars = [vars repmat(i,1,js(i))];
        end
    end
    k = setdiff1D(1:n,ii);
    if isempty(k)
        p_base_parametric = p_base_parametric.*sparse(ii,repmat(1,1,n),params(vars));
    else
        pp = params(vars); % Must do this, bug in ML 6.1 (x=sparse(1);x([1 1]) gives different result in 6.1 and 7.0!)
        temp = sparse([ii k(:)'],repmat(1,1,n),[pp(:)' ones(1,1,length(k))]);
        p_base_parametric = p_base_parametric.*temp;
    end
else
    % Bummer, nonlinear parameterization sucks...
    for i = 1:length(p_base)
        j = find(exponent_p(i,ParametricIndicies));
        if ~isempty(j)
            temp = p_base(i);
            for k = 1:length(j)
                if exponent_p(i,ParametricIndicies(j(k)))==1
                    temp = temp*params(j(k));
                else
                    temp = temp*params(j(k))^exponent_p(i,ParametricIndicies(j(k)));
                end
            end
            xx{i} = temp;
        else
            xx{i} = p_base(i);
        end
    end
    p_base_parametric = stackcell(sdpvar(1,1),xx)';
end



% 
% 
% 
% 
% 
% function [base,v] = coefficients(p,x)
% %COEFFICIENTS Extract coefficients and monomials from polynomials
% %
% %   [c,v] = COEFFICIENTS(p,x) extracts the coefficents
% %   of a polynomial p(x) = c'*v(x)
% %
% %   INPUT
% %    p : SDPVAR object
% %    x : SDPVAR object
% %
% %   OUTPUT
% %    c : SDPVAR object
% %    v : SDPVAR object
% %
% %   EXAMPLE
% %    sdpvar x y s t                
% %    p = x^2+x*y*(s+t)+s^2+t^2;     % define p(x,y), parameterized with s and t
% %    [c,v] = coefficients(p,[x y]); 
% %    sdisplay([c v]) 
% %
% %   See also SDPVAR
% 
% % Author Johan Löfberg
% % $Id: matrixcoefficients.m,v 1.4 2006/08/09 12:14:04 joloef Exp $  
% 
% 
% if length(p) > 1%size(p,2) > 1
%     error('Coefficents can only be applied to column vectors');
% end
% 
% allvar = depends(p);
% if nargin==1
%     xvar = allvar;
%     x = recover(xvar);
% else
%     xvar = depends(x);    
% end
% 
% pvar = recover(depends(p));
% 
% base = [];
% for i = 1:length(p)
%     [bi{i},vi{i}] = coefficientsi(p(i),xvar,pvar,allvar);
% end
% 
% % Fix the lengths of the basis to use same basis for all elements
% if length(bi)>1
%     allvars = [];
%     for i = 1:length(bi)
%         bivar{i} = getvariables(vi{i});
%         if isequal(vi{i}(1),1)
%             bivar{i} = [0 bivar{i}];
%         end
%         allvars = unique([allvars bivar{i}]);
%     end
%     v = recover(allvars);
%     c = zeros(length(p),length(allvars))';
%     ci = [];
%     cj = [];
%     cv = [];
%     for i = 1:length(bi)
%         index = find(ismember(allvars,bivar{i}));
%         ci = [ci index];
%         cj = [cj repmat(i,1,length(index))];
%         cv = [cv bi{i}'];        
%     end
%     base = sparse(ci,cj,cv);
% else
%     base = bi{1};
%     v = vi{1};
% end
% 
% 
% function [base,v] = coefficientsi(p,xvar,pvar,allvar)
% 
% % Try to debug this!
% t = setdiff(allvar,xvar);
% [exponent_p,p_base] = getexponentbase(p,pvar);
% ParametricIndicies = find(ismember(allvar,t));
% % FIX : don't define it here, wait until sparser below. Speed!!
% tempbase = parameterizedbase(p,[],recover(t),ParametricIndicies,exponent_p,p_base);
% [i,j,k] = unique(full(exponent_p(:,find(~ismember(allvar,t)))),'rows');
% %V = sparse(max(k),length(tempbase));
% %for i = 1:max(k)    
% %    V(i,find(k==i)) = 1;
% %end
% V = sparse(1:length(k),k,1,length(tempbase),max(k))';
% base = V*tempbase;
% if nargout == 2
%     keepthese = j(1:max(k));
%     v = recovermonoms(exponent_p(keepthese,find(~ismember(allvar,t))),recover(xvar));
% end
% 
% 
% function p_base_parametric = parameterizedbase(p,z, params,ParametricIndicies,exponent_p,p_base)
% 
% % Check for linear parameterization
% parametric_basis = exponent_p(:,ParametricIndicies);
% if all(sum(parametric_basis,2)==0)
%     p_base_parametric = full(p_base(:));
%     return
% end
% if all(sum(parametric_basis,2)<=1)
%     p_base_parametric = full(p_base(:));
%     n = length(p_base_parametric);
%     ii = [];
%     vars = [];
%     js = sum(parametric_basis,1);
%     for i = 1:size(parametric_basis,2)
%         if js(i)
%             j = find(parametric_basis(:,i));
%             ii = [ii j(:)'];
%             vars = [vars repmat(i,1,js(i))];
%         end
%     end
%     k = setdiff1D(1:n,ii);
%     if isempty(k)
%         p_base_parametric = p_base_parametric.*sparse(ii,repmat(1,1,n),params(vars));
%     else
%         pp = params(vars); % Must do this, bug in ML 6.1 (x=sparse(1);x([1 1]) gives different result in 6.1 and 7.0!)
%         p_base_parametric = p_base_parametric.*sparse([ii k(:)'],repmat(1,1,n),[pp(:)' ones(1,1,length(k))]);
%     end
% else
%     % Bummer, nonlinear parameterization sucks...
%     for i = 1:length(p_base)
%         j = find(exponent_p(i,ParametricIndicies));
%         if ~isempty(j)
%             temp = p_base(i);
%             for k = 1:length(j)
%                 if exponent_p(i,ParametricIndicies(j(k)))==1
%                     temp = temp*params(j(k));
%                 else
%                     temp = temp*params(j(k))^exponent_p(i,ParametricIndicies(j(k)));
%                 end
%             end
%             xx{i} = temp;
%         else
%             xx{i} = p_base(i);
%         end
%     end
%     p_base_parametric = stackcell(sdpvar(1,1),xx)';
% end