A Decomposition Augmented Lagrangian Method for Low-Rank Semidefinite Programming-Reference-Cited by-同舟云学术

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A Decomposition Augmented Lagrangian Method for Low-Rank Semidefinite Programming

Published:2023-07-12 Issue:3 Volume:33 Page:1361-1390
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Wang Yifei¹^ORCID,Deng Kangkang²^ORCID,Liu Haoyang²,Wen Zaiwen³

Affiliation:

1. Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA.

2. Beijing International Center for Mathematical Research, College of Engineering and Center for Machine Learning Research, Peking University, Peking 100084, China.

3. Corresponding author. Beijing International Center for Mathematical Research, Peking University, Peking 100871, China.

Funder

National Natural Science Foundation of China

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software,Applied Mathematics

Reference30 articles.

1. Adaptive regularization with cubics on manifolds

2. LP and SDP branch-and-cut algorithms for the minimum graph bisection problem: a computational comparison

3. Problems of distance geometry and convex properties of quadratic maps

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. A Feasible Method for General Convex Low-Rank SDP Problems;SIAM Journal on Optimization;2024-07-01

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