Minor cleaning updates

Functions/LowRAMP_UV.m

@@ -97,9 +97,9 @@
 
     u_old=zeros(m,RANK);
     v_old=zeros(n,RANK);
-    u_var=zeros(RANK,RANK);u_var_old=zeros(RANK,RANK);
-    v_var=zeros(RANK,RANK);v_var_old=zeros(RANK,RANK);
-
+    u_var=zeros(RANK,RANK);
+    v_var=zeros(RANK,RANK);
+    
     A_u=zeros(RANK,RANK);
     B_u=zeros(m,RANK);
     A_v=zeros(RANK,RANK);
@@ -125,19 +125,12 @@
         B_u_new=(S*v)/sqrt(n)-u_old*v_var/(Delta);
         A_u_new=v'*v/(n*Delta);
 
-      %  m_mean=sum(sum(triu(A_u_new,1)))/((RANK*RANK-RANK)*0.5);    
-      %  A_u_new=diag(diag(A_u_new))+triu(ones(RANK,RANK),1)*m_mean+triu(ones(RANK,RANK),1)'*m_mean;
-
-
         B_v_new=(S'*u)/sqrt(n)-v_old*(m*u_var/n)/(Delta);
         A_v_new=u'*u/(n*Delta);
-
-     %   m_mean=sum(sum(triu(A_v_new,1)))/((RANK*RANK-RANK)*0.5);    
-     %   A_v_new=diag(diag(A_v_new))+triu(ones(RANK,RANK),1)*m_mean+triu(ones(RANK,RANK),1)'*m_mean;
-
+
         %Keep old variables
-        u_old=u;u_var_old=u_var;
-        v_old=v;v_var_old=v_var;
+        u_old=u;
+        v_old=v;
 
         %Iteration with fixed damping or learner one
         pass=0;

Functions/LowRAMP_XX.m

@@ -93,10 +93,6 @@
         B_new=(S*x)/sqrt(n)-x_old*x_V/(Delta);
         A_new=x'*x/(n*Delta);
 
-        %Right, here i can play...
-    %   m_mean=sum(sum(triu(A_new,1)))/((RANK*RANK-RANK)*0.5);    
-    %   A_new=diag(diag(A_new))+triu(ones(RANK,RANK),1)*m_mean+triu(ones(RANK,RANK),1)'*m_mean;
-
         %Keep old variables
         x_old=x;
 

README

@@ -18,7 +18,17 @@ Files included in this package :
     subroutines folder : all subroutines needed by the different parts of the algorithm. 
 
 USAGE : from matlab, includes the two subfolders Functions and Subroutines (for instance using path(path,'./Functions');
-path(path,'./Subroutines'); path(path,'./Functions');). Then  run the demo demo_LowRAMP_UV, demo_PCA_and_completion or demo_LowRAMP_XX
+path(path,'./Subroutines'); path(path,'./Functions');). 
+
+Then run 
+* demo demo_LowRAMP_UV for a UV factorization of a clustering  problem
+* demo_PCA_and_completion for a completion problem
+* demo_LowRAMP_XX for a community detection problem
+* demo_Rank1_Submatrix for a submatrix localization problem
 
 These equations are based on http://arxiv.org/abs/1503.00338 (ISIT 2015) and http://arxiv.org/abs/1507.03857
+They follow from earlier works: 
+http://arxiv.org/abs/1402.2238
+http://arxiv.org/pdf/1202.2759.pdf
+http://papers.nips.cc/paper/5074-low-rank-matrix-reconstruction-and-clustering-via-approximate-message-passing
 Comments and remarks regarding bugs or functionalities are more than welcome.

demo_Rank1_Sumatrix.m → demo_Rank1_Submatrix.m

@@ -7,7 +7,7 @@
 Delta=0.001;%Variance of the gaussian noise
 RANK=1;%rank
 
-fprintf(1,'Creating a %dx%d signal with a %dx%d submtrix hidden \n',m,n,m_m,n_n);
+fprintf(1,'Creating a %dx%d signal with a %dx%d submatrix hidden \n',m,n,m_m,n_n);
 Y=[zeros(m-m_m,n); ones(m_m,n_n) zeros(m_m,n-n_n)];
 
 subplot(2,2,1)