Zaiwen Wen
Peking University
115 Papers
608 Citations
Zaiwen Wen is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Semidefinite programming. The author has an hindex of 29, co-authored 101 publications. Previous affiliations of Zaiwen Wen include Columbia University & Shanghai Jiao Tong University.
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Papers
A feasible method for optimization with orthogonality constraints
Zaiwen Wen,Wotao Yin +1 more
TL;DR: The Cayley transform is applied—a Crank-Nicolson-like update scheme—to preserve the constraints and based on it, curvilinear search algorithms with lower flops are developed with high efficiency for polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems.
Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
Zaiwen Wen,Wotao Yin,Yin Zhang +2 more
TL;DR: A low-rank factorization model is proposed and a nonlinear successive over-relaxation (SOR) algorithm is constructed that only requires solving a linear least squares problem per iteration to improve the capacity of solving large-scale problems.
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
TL;DR: An algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known (to a certain accuracy).
Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
Yuan Shen,Zaiwen Wen,Yin Zhang +2 more
TL;DR: Numerical studies indicate that the effectiveness of the proposed model is limited to problems where the sparse matrix does not dominate the low-rank one in magnitude, but results show that the proposed method in general has a much faster solution speed than nuclear-norm minimization algorithms and often provides better recoverability.
Alternating direction methods for classical and ptychographic phase retrieval
TL;DR: It is shown how the augmented Lagrangian alternating direction method can be used to solve both the classical and ptychographic phase retrieval problems, and its performance against standard algorithms for phase retrieval on a number of test images is compared.
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