5 papers
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…
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…