Please use this identifier to cite or link to this item: https://doi.org/10.1137/080738970
Title: A singular value thresholding algorithm for matrix completion
Authors: Cai, J.F.
Candès, E.J.
Shen, Z. 
Keywords: Lagrange dual function
Linearized Bregman iteration
Matrix completion
Nuclear norm minimization
Singular value thresholding
Uzawa's algorithm
Issue Date: 2010
Citation: Cai, J.F., Candès, E.J., Shen, Z. (2010). A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization 20 (4) : 1956-1982. ScholarBank@NUS Repository. https://doi.org/10.1137/080738970
Abstract: This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem and arises in many important applications as in the task of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative, produces a sequence of matrices {X k,Y k}, and at each step mainly performs a soft-thresholding operation on the singular values of the matrix Y k. There are two remarkable features making this attractive for low-rank matrix completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix; the second is that the rank of the iterates {X k} is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal storage space and keep the computational cost of each iteration low. On the theoretical side, we provide a convergence analysis showing that the sequence of iterates converges. On the practical side, we provide numerical examples in which 1, 000 × 1, 000 matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate that our approach is amenable to very large scale problems by recovering matrices of rank about 10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are connected with the recent literature on linearized Bregman iterations for-1 minimization, and we develop a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms. Copyright © 2010, Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/102764
ISSN: 10526234
DOI: 10.1137/080738970
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