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.

Last update: Apr 08, 2022

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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

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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.

Related tags

Overview

WARPd

Owner

Matthew Colbrook

use machine learning to recognize gesture on raspberrypi

Genetic feature selection module for scikit-learn

DI-HPC is an acceleration operator component for general algorithm modules in reinforcement learning algorithms

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Utility code for use with PyXLL

This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

deep-prae

YKKDetector For Python

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