Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

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Deep LearningWARPd
Overview

WARPd

Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

The paper: "WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditions"

Contents of code:

Main routines:
WARPd.m: main routine for the algorithm.
WARPdSR.m: noise-blind recovery version based on additional square-root LASSO term
WARPd_mc.m: version for matrix completion that uses PROPACK
WARPd_reweight.m and WARPdSR_reweight.m: iterative reweighting versions used for final numerical experiments

Example code of how to use main routines:
matrix_completion_example.m
shearlet_TVG_example.m

Owner
Matthew Colbrook
PhD student at DAMTP, University of Cambridge
Matthew Colbrook
GitHub Repository
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