Description
In this page, we include the Matlab code associated with the paper Gustavo Silva, Paul Rodriguez "Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse problems", 2020.
Matlab Code
The matlab code provides a usage example in the demo folder. The core files in the package are FISTA_APGCns_cbpdndl.m, FISTA_APGCns_cbpdndl_gpu.m and APGCns_Anomaly_detection.m.
[D, X, optinf] = FISTA_APGCns_cbpdndl(D0, S, lambda, opt)
[D, X, optinf] = FISTA_APGCns_cbpdndl_gpu(D0, S, lambda, opt)
[X, E, optinf] = APGCns_Anomaly_detection(D, S, lambda, beta, opt)
FISTA_APGCns_cbpdndl and FISTA_APGCns_cbpdndl_gpu : D0 is the initial dictionary, S represents the training set, lambda is the sparsity parameter and opt is an array that contains fixed values of configuration.
APGCns_Anomaly_detection : D is a given dictionary, S is the signal data that has anomalies, lambda and beta are the sparsity parameters and opt is an array that contains fixed values of configuration.