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943. Loraine – An Interior-Point Solver for Low-Rank Semidefinite Programming
Invited abstract in session MD-34: Preconditioning for Large Scale Nonlinear Optimization, stream Advances in large scale nonlinear optimization.
Monday, 14:30-16:00Room: 43 (building: 303A)
Authors (first author is the speaker)
| 1. | Soodeh Habibi
|
| University of Liverpool | |
| 2. | Michal Kocvara
|
| School of Mathematics, University of Birmingham | |
| 3. | Michael Stingl
|
| Institut fuer Angewandte Mathematik 2, Friedrich-Alexander-Universitaet-Erlangen-Nuernberg |
Abstract
The aim of this talk is to introduce Loraine, 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 will be demonstrated by numerical experiments using the truss topology optimization problems, Lasserre relaxations of the MAXCUT problems, and the sensor network localization problems. The code is available in Matlab and Julia, and it can be used not only for low-rank problems but also for any linear SDP.
Keywords
- Interior Point Methods
- Convex Optimization
Status: accepted
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