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Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion

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arxiv 2008.04988 v1 pith:QWDT2U4A submitted 2020-08-11 cs.LG cs.NAmath.NAstat.ML

Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion

classification cs.LG cs.NAmath.NAstat.ML
keywords asymptoticmatrixratealternatingboundcompletionconvergenceentries
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We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries. We bound the asymptotic convergence rate by the variational characterization of eigenvalues of a reversible consensus problem. This leads to a polynomial upper bound on the asymptotic rate in terms of number of nodes as well as the largest degree of the graph of revealed entries.

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