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Article Dans Une Revue SIAM Journal on Mathematics of Data Science Année : 2021

Scalable Semidefinite Programming

Résumé

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.

Dates et versions

hal-02493338 , version 1 (27-02-2020)

Identifiants

Citer

Alp Yurtsever, Joel A. Tropp, Olivier Fercoq, Madeleine Udell, Volkan Cevher. Scalable Semidefinite Programming. SIAM Journal on Mathematics of Data Science, 2021, ⟨10.1137/19M1305045⟩. ⟨hal-02493338⟩
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