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• W2174754147 abstract "Significance Modern data analysis requires solving hard optimization problems with a large number of parameters and a large number of constraints. A successful approach is to replace these hard problems by surrogate problems that are convex and hence tractable. Semidefinite programming relaxations offer a powerful method to construct such relaxations. In many instances it was observed that a semidefinite relaxation becomes very accurate when the noise level in the data decreases below a certain threshold. We develop a new method to compute these noise thresholds (or phase transitions) using ideas from statistical physics." @default.
• W2174754147 created "2016-06-24" @default.
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• W2174754147 date "2016-03-21" @default.
• W2174754147 modified "2023-09-27" @default.
• W2174754147 title "Phase transitions in semidefinite relaxations" @default.
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• W2174754147 doi "https://doi.org/10.1073/pnas.1523097113" @default.
• W2174754147 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4843421" @default.
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• W2174754147 hasPublicationYear "2016" @default.
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