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

GitHub Repository

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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

A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.

Trying to understand alias-free-gan.

Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT)

Temporal Knowledge Graph Reasoning Triggered by Memories

[CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

CIFAR-10 Photo Classification

Winners of DrivenData's Overhead Geopose Challenge

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