Zhou, Fengyu and Low, Steven H. (2021) Conditions for Exact Convex Relaxation and No Spurious Local Optima. IEEE Transactions on Control of Network Systems, 9 (3). pp. 1468-1480. ISSN 2325-5870. doi:10.1109/TCNS.2021.3112758. https://resolver.caltech.edu/CaltechAUTHORS:20210510-075841014
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Abstract
Non-convex optimization problems can be approximately solved via relaxation or local algorithms. For many practical problems such as optimal power flow (OPF) problems, both approaches tend to succeed in the sense that relaxation is usually exact and local algorithms usually converge to a global optimum. In this paper, we study conditions which are sufficient or necessary for such non-convex problems to simultaneously have exact relaxation and no spurious local optima. Those conditions help us explain the widespread empirical experience that local algorithms for OPF problems often work extremely well.
| Item Type: | Article | |||||||||
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| Additional Information: | © 2021 IEEE. | |||||||||
| Subject Keywords: | Convex relaxation, local optimum, optimal power flow, semidefinite program | |||||||||
| Issue or Number: | 3 | |||||||||
| DOI: | 10.1109/TCNS.2021.3112758 | |||||||||
| Record Number: | CaltechAUTHORS:20210510-075841014 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20210510-075841014 | |||||||||
| Official Citation: | F. Zhou and S. Low, "Conditions for Exact Convex Relaxation and No Spurious Local Optima," in IEEE Transactions on Control of Network Systems, doi: 10.1109/TCNS.2021.3112758 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 109018 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | Tony Diaz | |||||||||
| Deposited On: | 10 May 2021 19:56 | |||||||||
| Last Modified: | 03 Oct 2022 23:26 |
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