ivan@155: function [Phiout,unhatnz] = dict_update_REG_cn(Phi,x,unhat,maxIT,eps,cvset)
ivan@155: %% Regularized Dictionary Learning with the constraint on the column norms %%%%%
ivan@155: % Phi = Normalized Initial Dictionary
ivan@155: % x = Signal(x). This can be a vector or a matrix
ivan@155: % unhat = Initial guess for the coefficients
ivan@155: % to = 1/(step size) . It is larger than spectral norm of coefficient matrix x
ivan@155: % eps = Stopping criterion for iterative softthresholding and MM dictionary update
ivan@155: % cvset = Dictionary constraint. 0 = Non convex ||d|| = 1, 1 = Convex ||d||<=1
ivan@155: % Phiout = Updated Dictionary
ivan@155: % unhatnz Updated Coefficients (the same as input in this version)
ivan@155: 
ivan@155: %%
ivan@155: B = Phi;
ivan@155: K = zeros(size(Phi,1),size(Phi,2));
ivan@155: c = .1 + svds(unhat,1)^2; %.1
ivan@155: c3 = (1/c)*eye(size(B,2));
ivan@155: c1 = x*unhat'*c3;
ivan@155: c2 = (c*eye(size(B,2))-unhat*unhat')*c3;
ivan@155: 
ivan@155: %%
ivan@155:       
ivan@155: for i=1:maxIT
ivan@155: %     if i>1
ivan@155: %         B = K;
ivan@155: %     end
ivan@155: 
ivan@155:     K = c1 + B*c2;
ivan@155:     
ivan@155:     if cvset == 1,
ivan@155:         K = K*diag(min(sum(K.^2).^(-.5),1)); % with convex constraint set
ivan@155:     else
ivan@155:         % Mehrdad original - 
ivan@155:         % K = K*diag(sum(K.^2).^(-.5)); % with fixed-norm constraint set
ivan@155:         K = normc(K);
ivan@155:     end
ivan@155:        
ivan@155:     if (sum(sum((B-K).^2)) < eps)
ivan@155:         break;
ivan@155:     end
ivan@155:     
ivan@155:     B = K;
ivan@155: end
ivan@155: %% depleted atoms cancellation %%%
ivan@155: [Y,I] = sort(sum(K.^2),'descend');
ivan@155: RR = sum(Y>=.01);
ivan@155: Phiout = K(:,I(1:RR));
ivan@155: unhatnz = unhat(I(1:RR),:);
ivan@155: end