7 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 Optimization
Jiachen Jin, Kangkang Deng, Hongxia Wang
We study a class of nonsmooth stochastic optimization problems on Riemannian manifolds. In this work, we propose MARS-ADMM, the first stochastic Riemannian alternating direction me…
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…
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…