Sublinear time algorithms for approximate semidefinite programming
We consider semidefinite optimization in a saddle point formulation where the primal solution is in the spectrahedron and the dual solution is a distribution over affine functions. We present an approximation algorithm for this problem that runs in sublinear time in the size of the data. To the best...
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| Veröffentlicht in: | Mathematical programming Jg. 158; H. 1-2; S. 329 - 361 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.07.2016
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0025-5610, 1436-4646 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | We consider semidefinite optimization in a saddle point formulation where the primal solution is in the
spectrahedron
and the dual solution is a distribution over affine functions. We present an approximation algorithm for this problem that runs in sublinear time in the size of the data. To the best of our knowledge, this is the first algorithm to achieve this. Our algorithm is also guaranteed to produce low-rank solutions. We further prove lower bounds on the running time of any algorithm for this problem, showing that certain terms in the running time of our algorithm cannot be further improved. Finally, we consider a non-affine version of the saddle point problem and give an algorithm that under certain assumptions runs in sublinear time. |
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| Bibliographie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| ISSN: | 0025-5610 1436-4646 |
| DOI: | 10.1007/s10107-015-0932-z |