A Singular Value Thresholding Algorithm for Matrix Completion

TL;DR

This paper presents an efficient first-order algorithm for large-scale low-rank matrix completion, leveraging singular value soft-thresholding to recover matrices from limited data with minimal computational resources.

Contribution

It introduces a simple, scalable algorithm for matrix completion that is particularly effective for large problems with low-rank solutions, improving over traditional methods.

Findings

Recovered 1,000x1,000 matrices in under a minute.

Successfully completed matrix recovery with nearly a billion unknowns.

Demonstrated effectiveness on matrices with rank about 10 from 0.4% sampled entries.

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 and 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. We provide numerical examples in which 1,000 by 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 linearized Bregman iterations for l1 minimization, and we develop a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.