version 1.7.3

Author: Nathanael Perraudin

demos/demo_fbb_primal_dual.m

@@ -1,4 +1,4 @@
-%DEMO_FBB_PRIMAL_DUAL  Example of use of the forward backward based primal dual  solver
+%DEMO_FBB_PRIMAL_DUAL Example of use of the forward backward based primal dual  solver
 %
 %   We present an example of the the forward backward based primal dual
 %   solver through an image de-noising, in-painting problem. We express the
@@ -20,7 +20,7 @@
 %
 %      Results
 %
-%   
+%      
 %
 %   References: komodakis2014playing
 
@@ -96,8 +96,8 @@
 imagesc_gray(noisy_img, 1, '(b) Noisy image',222,[0 1]);
 imagesc_gray(b, 1, '(c) Measurements',223,[0 1]);
 imagesc_gray(sol, 1, '(d) Solution of optimization',224,[0 1]);  
-paramplot.position = [100 100 500 500];
-gsp_plotfig('inpainting_fbb_primal_dual',paramplot);
+%paramplot.position = [100 100 500 500];
+%gsp_plotfig('inpainting_fbb_primal_dual',paramplot);
 
 %% Closing the toolbox
 close_unlocbox();

mat2doc/ignore

@@ -1,3 +1,5 @@
 demo_lrjs
 solve_lrjs
-solver
+solver
+squareform
+pdist

test_bench/test_solvers.m

@@ -1390,15 +1390,14 @@
     f21.prox = @(x,T) prox_l1(x,T,paraml11);    
     f21.L = A;
     f21.Lt = At;
-    f21.norm_L = 1;
+    f21.norm_L = norm(Mat)^2;
 
     paraml12.verbose = 0;
     f22.eval = @(x) norm(x,1);
     f22.prox = @(x,T) prox_l1(x,T,paraml12);
 
     param.tol = 100*eps;
     param.verbose = 1;
-    param.gamma = 0.1;
     param.maxit = 1000;
 
     paramb2.verbose = 0;

unlocbox_version

@@ -1 +1 @@
-1.7.2
+1.7.3

utils/Contents.m

@@ -31,6 +31,8 @@
 %    vec                -  Vectorize
 %    svdecon            -  Acceleleration of svd
 %    svdsecon           -  Acceleleration of svds
+%    sum_squareform     -  Sparse matrix that sums the squareform of a vector
+%    squareform_sp      -  Sparse counterpart of matlab's squareform
 %
 %  For help, bug reports, suggestions etc. please send email to
 %  unlocbox (at) groupes (dot) epfl (dot) ch

utils/squareform_sp.m

@@ -10,19 +10,18 @@
 %       W: matrix form of input vector w OR
 %       w: vector form of input matrix W
 %
-%
 %   This function is to be used instead of squareform.m when the matrix W
 %   or the vector w is sparse. For large scale computations, e.g. for
 %   learning the graph structure of a big graph it is necessary to take
 %   into account the sparsity.
 %
 %   Example:::
 %
-%   B = sprand(8, 8, 0.1);
-%   B = B+B';
-%   B(1:9:end) = 0;
-%   b = squareform_sp(B);
-%   Bs = squareform_sp(b);
+%       B = sprand(8, 8, 0.1);
+%       B = B+B';
+%       B(1:9:end) = 0;
+%       b = squareform_sp(B);
+%       Bs = squareform_sp(b);
 %
 %   See also: squareform sum_squareform pdist
 
@@ -99,3 +98,5 @@
     new_ind = ind_j + (ind_i-1)*n - ind_i.*(ind_i+1)/2;
     w = sparse(new_ind, 1, s, n*(n-1)/2, 1);
 end    
+
+end

utils/sum_squareform.m

@@ -1,5 +1,5 @@
 function [S, St] = sum_squareform(n, mask)
-%SUM_SQUAREFORM: sparse matrix that sums the squareform of a vector
+%SUM_SQUAREFORM sparse matrix that sums the squareform of a vector
 %   Usage:  [S, St] = sum_squareform(n)
 %           [S, St] = sum_squareform(n, mask)
 %
@@ -25,17 +25,16 @@
 %   * sum(S) = 2*ones(1, n*(n-1)/2)
 %   * sum(St) = sum(squareform(mask))   -- for full mask = (n-1)*ones(n,1)
 %
+%   Example::
+%           % if mask is given, the resulting S are the ones we would get with the
+%           % following operations (but memory efficiently):
+%           [S, St] = sum_squareform(n);
+%           [ind_i, ~, w] = find(mask(:));
+%           % get rid of the columns of S corresponding to zeros in the mask
+%           S = S(:, ind_i);
+%           St = St(ind_i, :);
 %
-%   Example: 
-%   % if mask is given, the resulting S are the ones we would get with the
-%   % following operations (but memory efficiently):
-%   [S, St] = sum_squareform(n);
-%   [ind_i, ~, w] = find(mask(:));
-%   % get rid of the columns of S corresponding to zeros in the mask
-%   S = S(:, ind_i);
-%   St = St(ind_i, :);
-%
-%   See also squareform_sp
+%   See also: squareform_sp
 %
 
 %
@@ -119,3 +118,5 @@
 
 St = sparse([1:ncols, 1:ncols], [I, J], 1, ncols, n);
 S = St';
+
+end