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Scalable Semidefinite Programming

Abstract : Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct algorithm for solving large SDP problems by economizing on both the storage and the arithmetic costs. Numerical evidence shows that the method is effective for a range of applications, including relaxations of MaxCut, abstract phase retrieval, and quadratic assignment. Running on a laptop, the algorithm can handle SDP instances where the matrix variable has over $10^{13}$ entries.
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Preprints, Working Papers, ...
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Contributor : Olivier Fercoq <>
Submitted on : Thursday, February 27, 2020 - 4:50:17 PM
Last modification on : Thursday, October 15, 2020 - 9:54:24 PM

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  • HAL Id : hal-02493338, version 1
  • ARXIV : 1912.02949



Alp Yurtsever, Joel A. Tropp, Olivier Fercoq, Madeleine Udell, Volkan Cevher. Scalable Semidefinite Programming. 2020. ⟨hal-02493338⟩



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