| | \n\n \n \n \n alpha= | \tsmoothing parameter, typical value: 1 to 10 times estimated norm(x,inf) \n | \n \n\n \n \n \n delta=0.0001 | \tdelta controls update step and convergent, small delta ensure convergence but with small decrease in data fit error \n | \n \n\n \n \n\n \n \n\n \n \n\n \n \n\n \n \n\n \n \n \n lamda=1000. | \tlamda controls sparsity, bigger lamda, more sparsity \n | \n \n\n \n \n \n lip= | \tthe estimated Lipschitz constrant of the dual objective, default: alpha*normest(A*A\',1e-2) \n | \n \n\n \n \n\n \n \n \n file mres= | \tauxiliary output file name \n | \n \n\n \n \n\n \n \n\n \n \n\n \n \n\n \n \n\n \n \n \n file res= | \tauxiliary output file name \n | \n \n\n \n \n \n reset= | \tNesterov\'s acceleration restart (theta is reset) or skip (theta is not reset) \n | \n \n\n \n \n\n \n \n \n savevel=0 | \tFlag to choose the algorithm \n | \n \n\n \n \n\n \n \n \n step=0.000005 | \tstep is very important in convergence and sparsity \n | \n \n\n \n \n \n file vel0= | \tauxiliary input file name \n | \n \n\n \n \n \n file velout= | \tauxiliary output file name \n | \n \n |