1 citations · 1 across the 8 of their papers we have counts for
9 papers
Stochastic Block Bregman Projection with Polyak-like Stepsize for Possibly Inconsistent Convex Feasibility Problems
Lu Zhang, Hongzhen Chen, Hongxia Wang +1
Stochastic projection algorithms for solving convex feasibility problems (CFPs) have attracted considerable attention due to their broad applicability. In this paper, we propose a…
Adaptive Momentum via Minimal Dual Function for Accelerating Randomized Sparse Kaczmarz
Lu Zhang, Jinchuan Zeng, Hongxia Wang +1
Recently, the randomized sparse Kaczmarz method has been accelerated by designing heavy ball momentum adaptively via a minimal-error principle. In this paper, we develop a new adap…
A Single-loop Stochastic Riemannian ADMM for Nonsmooth Composite Optimization
Jiachen Jin, Kangkang Deng, Hongxia Wang
We study a class of nonsmooth composite stochastic optimization on Riemannian manifolds, where the objective is the sum of an expectation function and a nonsmooth regularizer. Thes…
Adaptive Riemannian ADMM for Nonsmooth Optimization: Optimal Complexity without Smoothing
Kangkang Deng, Jiachen Jin, Jiang Hu +1
We study the problem of minimizing the sum of a smooth function and a nonsmooth convex regularizer over a compact Riemannian submanifold embedded in Euclidean space. By introducing…
Stochastic ADMM with batch size adaptation for nonconvex nonsmooth optimization
Jiachen Jin, Kangkang Deng, Boyu Wang +1
Stochastic alternating direction method of multipliers (SADMM) is a popular method for solving nonconvex nonsmooth optimization in various applications. However, it typically requi…
Stochastic momentum ADMM for nonconvex and nonsmooth optimization with application to PnP algorithm
Kangkang Deng, Shuchang Zhang, Boyu Wang +3
This paper proposes SMADMM, a single-loop Stochastic Momentum Alternating Direction Method of Multipliers for solving a class of nonconvex and nonsmooth composite optimization prob…
Inexact Riemannian Gradient Descent Method for Nonconvex Optimization
Juan Zhou, Kangkang Deng, Hongxia Wang +1
Gradient descent methods are fundamental first-order optimization algorithms in both Euclidean spaces and Riemannian manifolds. However, the exact gradient is not readily available…
Anderson acceleration of derivative-free projection methods for constrained monotone nonlinear equations
Jiachen Jin, Hongxia Wang, Kangkang Deng
The derivative-free projection method (DFPM) is an efficient algorithm for solving monotone nonlinear equations. As problems grow larger, there is a strong demand for speeding up t…
Decentralized Douglas-Rachford splitting methods for smooth optimization over compact submanifolds
Kangkang Deng, Jiang Hu, Hongxia Wang
We study decentralized smooth optimization problems over compact submanifolds. Recasting it as a composite optimization problem, we propose a decentralized Douglas-Rachford splitti…