[2101.08208] Solving SDP Faster: A Robust IPM Framework and Efficient Implementa...
source link: https://arxiv.org/abs/2101.08208
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[Submitted on 20 Jan 2021 (v1), last revised 18 Nov 2021 (this version, v2)]
Solving SDP Faster: A Robust IPM Framework and Efficient Implementation
This paper introduces a new robust interior point method analysis for semidefinite programming (SDP). This new robust analysis can be combined with either logarithmic barrier or hybrid barrier.
Under this new framework, we can improve the running time of semidefinite programming (SDP) with variable size n \times n and m constraints up to \epsilon accuracy.
We show that for the case m = \Omega(n^2), we can solve SDPs in m^{\omega} time. This suggests solving SDP is nearly as fast as solving the linear system with equal number of variables and constraints. This is the first result that tall dense SDP can be solved in the nearly-optimal running time, and it also improves the state-of-the-art SDP solver [Jiang, Kathuria, Lee, Padmanabhan and Song, FOCS 2020].
In addition to our new IPM analysis, we also propose a number of techniques that might be of further interest, such as, maintaining the inverse of a Kronecker product using lazy updates, a general amortization scheme for positive semidefinite matrices.
| Subjects: | Optimization and Control (math.OC); Data Structures and Algorithms (cs.DS) |
| Cite as: | arXiv:2101.08208 [math.OC] |
| (or arXiv:2101.08208v2 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2101.08208 |
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