Yurtsever, AlpTropp, Joel AaronFercoq, OlivierUdell, MadeleineCevher, Volkan2019-12-062019-12-062019-12-06202110.1137/19M1305045https://infoscience.epfl.ch/handle/20.500.14299/163818Semidefinite 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.Augmented Lagrangianconditional gradient methodconvex optimizationdimension reductionfirst-order methodFrank--Wolfe methodMaxCutphase retrievalquadratic assignmentrandomized linear algebrasemidefinite programmingsketchingtext::journal::journal article::research article