Z
Zaiwen Wen
Researcher at Peking University
Publications - 115
Citations - 5288
Zaiwen Wen is an academic researcher from Peking University. The author has contributed to research in topics: Semidefinite programming & Optimization problem. The author has an hindex of 29, co-authored 101 publications receiving 4356 citations. Previous affiliations of Zaiwen Wen include Columbia University & Shanghai Jiao Tong University.
Papers
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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.
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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.
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Alternating direction augmented Lagrangian methods for semidefinite programming
TL;DR: Numerical results for frequency assignment, maximum stable set and binary integer quadratic programming problems demonstrate that the algorithms are robust and very efficient due to their ability or exploit special structures, such as sparsity and constraint orthogonality in these problems.
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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.
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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).