Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Distance Matrix Completion by Numerical Optimization
Computational Optimization and Applications
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
SIAM Journal on Computing
Uncovering shared structures in multiclass classification
Proceedings of the 24th international conference on Machine learning
Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
SIAM Journal on Optimization
Probing the Pareto Frontier for Basis Pursuit Solutions
SIAM Journal on Scientific Computing
Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
SIAM Journal on Imaging Sciences
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
| Hi-index | 7.29 |
Recovering an unknown low-rank or approximately low-rank matrix from a sampling set of its entries is known as the matrix completion problem. In this paper, a nonlinear constrained quadratic program problem concerning the matrix completion is obtained. A new algorithm named the projected Landweber iteration (PLW) is proposed, and the convergence is proved strictly. Numerical results show that the proposed algorithm can be fast and efficient under suitable prior conditions of the unknown low-rank matrix.