WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions-Reference-Cited by-同舟云学术

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WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions

Published:2022-09 Issue:3 Volume:15 Page:1539-1575
ISSN:1936-4954
Container-title:SIAM Journal on Imaging Sciences
language:en
Short-container-title:SIAM J. Imaging Sci.

Author:

Colbrook Matthew J.^ORCID

Funder

Fondation Sciences Mathématiques de Paris

Trinity College, University of Cambridge

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,General Mathematics

Reference122 articles.

1. Correcting for unknown errors in sparse high-dimensional function approximation

2. The Gap between Theory and Practice in Function Approximation with Deep Neural Networks

3. Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. NESTANets: stable, accurate and efficient neural networks for analysis-sparse inverse problems;Sampling Theory, Signal Processing, and Data Analysis;2022-12-20

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