The two-stage iteration algorithms based on the shortest distance for low-rank matrix completion

Rui-Ping Wen and Li-Xia Liu

Applied Mathematics and Computation, 2017, vol. 314, issue C, 133-141

Abstract: Despite matrix completion requiring the global solution of a non-convex objective, there are many computational efficient algorithms which are effective for a broad class of matrices. Based on these algorithms for matrix completion with given rank problem, we propose a class of two-stage iteration algorithms for general matrix completion in this paper. The inner iteration is the scaled alternating steepest descent algorithm for the fixed-rank matrix completion problem presented by Tanner and Wei (2016), the outer iteration is used two iteration criterions: the gradient norm and the distance between the feasible part with the corresponding part of reconstructed low-rank matrix. The feasibility of the two-stage algorithms are proved. Finally, the numerical experiments show the two-stage algorithms with shorting the distance are more effective than other algorithms.

Keywords: Two-stage iteration; Alternating steepest descent algorithm; Low-rank matrix completion; Distance (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:314:y:2017:i:c:p:133-141

DOI: 10.1016/j.amc.2017.07.024

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