7 papers
Relaxed Greedy Randomized Kaczmarz with Signal Averaging for Solving Doubly-Noisy Linear Systems
Lu Zhang, Jinchuan Zeng, Hui Zhang
Large-scale linear systems of the form are often doubly-noisy, in the sense that both its measurement matrix and measurement vector are noisy. In this paper, we exte…
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…
Nonlinear preconditioned primal-dual method for a class of structured minimax problems
Lu Zhang, Hongxia Wang, Hui Zhang
We propose and analyze a general framework called nonlinear preconditioned primal-dual with projection for solving nonconvex-nonconcave and non-smooth saddle-point problems. The fr…
Quantile-based Random Sparse Kaczmarz for Corrupted and Noisy Linear Systems
Lu Zhang, Hongxia Wang, Hui Zhang
The randomzied Kaczmarz method, along with its recently developed variants, has become a popular tool for dealing with large-scale linear systems. However, these methods usually fa…
Revisiting Linearized Bregman Iterations under Lipschitz-like Convexity Condition
Hui Zhang, Lu Zhang, Hao-Xing Yang
The linearized Bregman iterations (LBreI) and its variants have received considerable attention in signal/image processing and compressed sensing. Recently, LBreI has been extended…
New insights in smoothness and strong convexity with improved convergence of gradient descent
Lu Zhang, Jiani Wang, Hui Zhang
The starting assumptions to study the convergence and complexity of gradient-type methods may be the smoothness (also called Lipschitz continuity of gradient) and the strong convex…