An Entropy-Regularized ADMM For Binary Quadratic Programming

We propose an entropy regularized splitting model using low-rank factorization for solving binary quadratic programming with linear inequality constraints. Different from the semidefinite programming relaxation model, our model preserves the rank-one constraint and aims to find high quality rank-one...

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Veröffentlicht in:	Journal of global optimization Jg. 87; H. 2-4; S. 447 - 479
Hauptverfasser:	Liu, Haoming, Deng, Kangkang, Liu, Haoyang, Wen, Zaiwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2023 Springer
Schlagworte:	Algorithms Computer Science Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Real Functions Alternating direction method of multipliers Entropy penalty Binary quadratic programming Riemannian manifold Low-rank factorization
ISSN:	0925-5001, 1573-2916
Online-Zugang:	Volltext
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