The Mixing method: low-rank coordinate descent for semidefinite
programming with diagonal constraints

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The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints

1 June 2017

Wei-Cheng Chang

J. Zico Kolter

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Papers citing "The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints"

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Title
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs Junhyung Lyle Kim Jose Antonio Lara Benitez Taha Toghani Cameron R. Wolfe Zhiwei Zhang Anastasios Kyrillidis 69 0 0 16 Jun 2021
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach Yu Chen Ji Zhao L. Kneip 46 31 0 22 Jan 2021
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets T. Roddenberry Santiago Segarra Anastasios Kyrillidis 52 0 0 17 Dec 2020
On Continuous Local BDD-Based Search for Hybrid SAT Solving Anastasios Kyrillidis Moshe Y. Vardi Zhiwei Zhang 29 8 0 14 Dec 2020
A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics Anirudha Majumdar G. Hall Amir Ali Ahmadi 115 102 0 14 Aug 2019
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver Po-Wei Wang P. Donti Bryan Wilder Zico Kolter LRM NAI 88 265 0 29 May 2019
Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method for Solving Large SDPs Murat A. Erdogdu Asuman Ozdaglar P. Parrilo N. D. Vanli 85 33 0 12 Jul 2018