fixed basic lambda update value

GLcuda.cu

@@ -464,4 +464,4 @@ __global__ void update_q(const float* __restrict__ Atb, const float* __restrict_
 extern "C" void update_vector_q(const float *Atb, const float *z, const float *u, float *q, const float rho,const int length){
 	update_q<<<(length+THREADS-1)/THREADS,THREADS>>>(Atb,z,u,q,rho,length);
 
-}
+}

GLmex.cpp

@@ -418,7 +418,7 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, mxArray const *prhs[]){//w
 				fabs(history_s_norm-last_s_norm)<0.00001f){
 					lambda_counter++;
 					if(lambda_counter>10){
-						_lambda*=0.01f;
+						_lambda*=0.1f;
 						lambda_counter=0;
 					}
 			}
@@ -474,4 +474,4 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, mxArray const *prhs[]){//w
 	//if(err!=cudaSuccess){printf("%s in %s at line %d\n",cudaGetErrorString(err),__FILE__,__LINE__);}
 
 	checkError(cublasDestroy(handle), "cublasDestroy() error!\n");
-}
+}

GenerateRandomGroupLassoDataSet.m

@@ -0,0 +1,52 @@
+function [ A,b,partition,lambda ] = GenerateRandomGroupLassoDataSet( m,K,density )% density default value should be 100
+
+flag=true;
+while flag
+    partition = int32(randi(225, [K 1]));% per Boyd example
+    n = int32(sum(partition)); % number of features
+    if (mod(n,16))==0
+        flag=false;
+    end
+end
+
+p = single(density/single(n));          % sparsity density
+
+% generate block sparse solution vector
+x = single(zeros(n,1));
+start_ind = int32(1);
+cum_part = int32(cumsum(single(partition)));
+
+for i = 1:K,
+    x(start_ind:cum_part(i)) = 0;
+    if( rand() < p)
+        % fill nonzeros
+        x(start_ind:cum_part(i)) = randn(partition(i),1);
+    end
+    start_ind = cum_part(i)+1;
+end
+
+% generate random data matrix
+A = randn(m,n);
+
+% normalize columns of A
+A = (A*spdiags(1./sqrt(sum(A.^2))',0,double(n),double(n)));
+A=full(single(A));
+% generate measurement b with noise
+b = single(A*x + sqrt(0.001)*randn(m,1));
+
+% lambda max
+start_ind = 1;
+lambdas=single(zeros(1,K+1));
+for i = 1:K,
+    sel = start_ind:cum_part(i);
+    lambdas(i) = norm(A(:,sel)'*b);
+    start_ind = cum_part(i) + 1;
+end
+lambda_max = max(lambdas);
+
+% regularization parameter
+lambda = 0.1*lambda_max;
+
+
+end
+