6 papers · 1 filter
A Retraction-free Method for Nonsmooth Minimax Optimization over a Compact Manifold
Necdet Serhat Aybat, Jiang Hu, Zhanwang Deng
We study the minimax problem , where is a compact submanifold, is continuously differentiable in , is a closed, weak…
SSNCVX: A primal-dual semismooth Newton method for convex composite optimization problem
Zhanwang Deng, Tao Wei, Jirui Ma +1
In this paper, we propose a uniform semismooth Newton-based algorithmic framework called SSNCVX for solving a broad class of convex composite optimization problems. By exploiting t…
Riemannian EXTRA: Communication-efficient decentralized optimization over compact submanifolds with data heterogeneity
Jiayuan Wu, Zhanwang Deng, Jiang Hu +2
We consider decentralized optimization over a compact Riemannian submanifold in a network of agents, where each agent holds a smooth, nonconvex local objective defined by its p…
An efficient primal dual semismooth Newton method for semidefinite programming
Zhanwang Deng, Jiang Hu, Kangkang Deng +1
In this paper, we present an efficient semismooth Newton method, named SSNCP, for solving a class of semidefinite programming problems. Our approach is rooted in an equivalent semi…
SLRQA: A Sparse Low-Rank Quaternion Model for Color Image Processing with Convergence Analysis
Zhanwang Deng, Yuqiu Su, Wen Huang
In this paper, we propose a Sparse Low-rank Quaternion Approximation (SLRQA) model for color image processing problems with noisy observations. %Different from the existing color i…
An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-Block Composite Optimization
Zhanwang Deng, Kangkang Deng, Jiang Hu +1
In this paper, we develop a novel primal-dual semismooth Newton method for solving linearly constrained multi-block convex composite optimization problems. First, a differentiable…