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3479. A predictor-corrector algorithm for semidefinite programming that uses the factor width cone
Invited abstract in session MB-38: Interior point methods, stream Conic Optimization: Theory, Algorithms, and Applications.
Monday, 10:30-12:00Room: 34 (building: 306)
Authors (first author is the speaker)
| 1. | Etienne De Klerk
|
| Econometrics and OR, Tilburg University |
Abstract
We propose an interior point method (IPM) for solving semidefinite programming problems (SDPs). The standard interior point algorithms used to solve SDPs work in the space of positive semidefinite matrices. Contrary to that the proposed algorithm works in the cone of matrices of constant factor width. We prove global convergence and provide a complexity analysis. Our work is inspired by a series of papers by Ahmadi, Dash, Majumdar and Hall, and builds upon a recent preprint by Roig-Solvas and Sznaier [arXiv:2202.12374, 2022]. This talk is based on joint work with Felix Kirschner.
Keywords
- Convex Optimization
- Interior Point Methods
Status: accepted
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