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• W1894571238 abstract "We propose imposing box constraints on the individual elements of the unknown matrix in the matrix completion problem and present a number of natural applications, ranging from collaborative ltering under interval uncertainty to computer vision. Moreover, we design an alternating direction parallel coordinate descent method (MACO) for a smooth unconstrained optimization reformulation of the problem. In large scale numerical experiments in collaborative ltering under uncertainty, our method obtains solution with considerably smaller errors compared to classical matrix completion with equalities. We show that, surprisingly, seemingly obvious and trivial inequality constraints, when added to the formulation, can have a large impact. This is demonstrated on a number of machine learning problems." @default.
• W1894571238 created "2016-06-24" @default.
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• W1894571238 date "2014-08-11" @default.
• W1894571238 modified "2023-09-27" @default.
• W1894571238 title "Inequality-Constrained Matrix Completion: Adding the Obvious Helps!" @default.
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• W1894571238 hasPublicationYear "2014" @default.
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