Loraine–an interior-point solver for low-rank semidefinite programming

Habibi S, Kočvara M, Stingl M (2023)


Publication Type: Journal article

Publication year: 2023

Journal

DOI: 10.1080/10556788.2023.2250522

Abstract

The aim of this paper is to introduce a new code for the solution of large-and-sparse linear semidefinite programs (SDPs) with low-rank solutions or solutions with few outlying eigenvalues, and/or problems with low-rank data. We propose to use a preconditioned conjugate gradient method within an interior-point SDP algorithm and an efficient preconditioner fully utilizing the low-rank information. The efficiency is demonstrated by numerical experiments using the truss topology optimization problems, Lasserre relaxations of the MAXCUT problems and the sensor network localization problems.

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How to cite

APA:

Habibi, S., Kočvara, M., & Stingl, M. (2023). Loraine–an interior-point solver for low-rank semidefinite programming. Optimization Methods & Software. https://doi.org/10.1080/10556788.2023.2250522

MLA:

Habibi, Soodeh, Michal Kočvara, and Michael Stingl. "Loraine–an interior-point solver for low-rank semidefinite programming." Optimization Methods & Software (2023).

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