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1975. Proximal-Stabilized Semidefinite Programming
Invited abstract in session TD-38: Semidefinite Programming and implementations, Quantum Information Theory and other applications, stream Conic Optimization: Theory, Algorithms, and Applications.
Tuesday, 14:30-16:00Room: 34 (building: 306)
Authors (first author is the speaker)
| 1. | Stefano Cipolla
|
| School of Mathematical Sciences, University of Southampton | |
| 2. | Jacek Gondzio
|
| School of Mathematics, University of Edinburgh |
Abstract
In this talk, we will present a regularized version of the primal-dual Interior Point Method (IPM) for the solution of Semidefinite Programming Problems (SDPs). Leveraging on the proximal point method, a novel Proximal Stabilized Interior Point Method for SDP (PS-SDP-IPM) is introduced. The method is strongly supported by theoretical results concerning its convergence: the worst-case complexity result is established for the inner regularized IPM solver. Moreover, the new method demonstrates an increased robustness when dealing with problems characterized by ill-conditioning or linear dependence of the constraints. Extensive numerical experience is reported to illustrate the advantages of the proposed method when compared to the state-of-the-art solver.
Keywords
- Programming, Semidefinite
- Interior Point Methods
- Continuous Optimization
Status: accepted
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