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Scripts_Sharing/ISFC/Utils_DL/spams-matlab/src_release/mexProximalPathCoding.m
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mexProximalPathCoding.m
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% Usage: [V [val_regularizer]]=mexProximalPathCoding(U,DAG,param);
%
% Name: mexProximalPathCoding
%
% Description: mexProximalPathCoding computes a proximal operator for
% the path coding penalties of http://arxiv.org/abs/1204.4539
%
% Given an input matrix U=[u^1,\ldots,u^n],
%
%
% Inputs: U: double p x n matrix (input signals)
% m is the signal size
% DAG: struct
% with three fields, weights, start_weights, stop_weights
% for a graph with |V| nodes and |E| arcs,
% DAG.weights: sparse double |V| x |V| matrix. Adjacency
% matrix. The non-zero entries represent costs on arcs
% linking two nodes.
% DAG.start_weights: dense double |V| vector. Represent the costs
% of starting a path from a specific node.
% DAG.stop_weights: dense double |V| vector. Represent the costs
% of ending a path at a specific node.
%
% if param.regul='graph-path-l0', non-convex penalty
% if param.regul='graph-path-conv', convex penalty
%
% param: struct
% param.lambda (regularization parameter)
% param.regul (choice of regularization, see above)
% param.verbose (optional, verbosity level, false by default)
% param.intercept (optional, last row of U is not regularized,
% false by default)
% param.pos (optional, adds positivity constraints on the
% coefficients, false by default)
% param.precision (optional, by default a very large integer.
% It returns approximate proximal operator by choosing a small integer,
% for example, 100 or 1000.
% param.numThreads (optional, number of threads for exploiting
% multi-core / multi-cpus. By default, it takes the value -1,
% which automatically selects all the available CPUs/cores).
%
% Output: V: double p x n matrix (output coefficients)
% val_regularizer: double 1 x n vector (value of the regularization
% term at the optimum).
%
% Author: Julien Mairal, 2012
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