13 citations · 55 across the 19 of their papers we have counts for
19 papers
On proximal gradient mapping and its minimization in norm via potential function-based acceleration
Beier Chen, Hui Zhang
The proximal gradient descent method, well-known for composite optimization, can be completely described by the concept of proximal gradient mapping. In this paper, we highlight ou…
Optimality Analysis and Block Sparse Algorithm for Complex Compressed Sensing
Hui Zhang, Xin Liu, Naihua Xiu
Recently, many new challenges in Compressed Sensing (CS), such as block sparsity, arose. In this paper, we present a new algorithm for solving CS with block sparse constraints (BSC…
Mirror frameworks for relatively Lipschitz and monotone-like variational inequalities
Hui Zhang, Yu-Hong Dai
Nonconvex-nonconcave saddle-point optimization in machine learning has triggered lots of research for studying non-monotone variational inequalities (VI). In this work, we introduc…
Linear convergence of random dual coordinate incremental aggregated gradient methods
Hui Zhang, Yu-Hong Dai, Lei Guo
In this paper, we consider the dual formulation of minimizing with the index sets and being large. To address the…
Inertial Bregman Proximal Gradient Algorithm For Nonconvex Problem with Smooth Adaptable Property
Xiaoya Zhang, Hui Zhang, Wei Peng
In this paper we study the problems of minimizing the sum of two nonconvex functions: one is differentiable and satisfies smooth adaptable property. The smooth adaptable property,…
A New Exact Worst-Case Linear Convergence Rate of the Proximal Gradient Method
Xiaoya Zhang, Hui Zhang
In this note, we establish a new exact worst-case linear convergence rate of the proximal gradient method in terms of the proximal gradient norm, which complements the recent resul…
Training GANs with Centripetal Acceleration
Wei Peng, Yuhong Dai, Hui Zhang +1
Training generative adversarial networks (GANs) often suffers from cyclic behaviors of iterates. Based on a simple intuition that the direction of centripetal acceleration of an ob…
Global Complexity Analysis of Inexact Successive Quadratic Approximation methods for Regularized Optimization under Mild Assumptions
Wei Peng, Hui Zhang, Xiaoya Zhang
Successive quadratic approximations (SQA) are numerically efficient for minimizing the sum of a smooth function and a convex function. The iteration complexity of inexact SQA metho…
Nonconvex Proximal Incremental Aggregated Gradient Method with Linear Convergence
Wei Peng, Hui Zhang, Xiaoya Zhang
In this paper, we study the proximal incremental aggregated gradient(PIAG) algorithm for minimizing the sum of L-smooth nonconvex component functions and a proper closed convex fun…
Inertial Proximal Incremental Aggregated Gradient Method
Xiaoya Zhang, Wei Peng, Hui Zhang +1
In this paper, we introduce an inertial version of the Proximal Incremental Aggregated Gradient method (PIAG) for minimizing the sum of smooth convex component functions and a poss…
Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence under Bregman Distance Growth Conditions
Hui Zhang, Yu-Hong Dai, Lei Guo +1
We introduce a unified algorithmic framework, called proximal-like incremental aggregated gradient (PLIAG) method, for minimizing the sum of a convex function that consists of addi…
Linear Convergence of the Proximal Incremental Aggregated Gradient Method under Quadratic Growth Condition
Hui Zhang
Under the strongly convex assumption, several recent works studied the global linear convergence rate of the proximal incremental aggregated gradient (PIAG) method for minimizing t…
On the Linear Convergence of the Cauchy Algorithm for a Class of Restricted Strongly Convex Functions
Hui Zhang
In this short note, we extend the linear convergence result of the Cauchy algorithm, derived recently by E. Klerk, F. Glineur, and A. Taylor, from the case of smooth strongly conve…
New Analysis of Linear Convergence of Gradient-type Methods via Unifying Error Bound Conditions
Hui Zhang
This paper reveals that a common and central role, played in many error bound (EB) conditions and a variety of gradient-type methods, is a residual measure operator. On one hand, b…
The restricted strong convexity revisited: Analysis of equivalence to error bound and quadratic growth
Hui Zhang
The restricted strong convexity is an effective tool for deriving globally linear convergence rates of descent methods in convex minimization. Recently, the global error bound and…
Variants of alternating minimization method with sublinear rates of convergence for convex optimization
Hui Zhang, Lizhi Cheng
The alternating minimization (AM) method is a fundamental method for minimizing convex functions whose variable consists of two blocks. How to efficiently solve each subproblems wh…
Projected shrinkage algorithm for box-constrained L1-minimization
Hui Zhang, Lizhi Cheng
Box-constrained L1-minimization can perform remarkably better than classical L1-minimization when correction box constraints are available. And also many practical L1-minimization…
A dual algorithm for a class of augmented convex models
Hui Zhang, Lizhi Cheng, Wotao Yin
Convex optimization models find interesting applications, especially in signal/image processing and compressive sensing. We study some augmented convex models, which are perturbed…
Strongly Convex Programming for Exact Matrix Completion and Robust Principal Component Analysis
Hui Zhang, Jian-Feng Cai, Lizhi Cheng +1
The common task in matrix completion (MC) and robust principle component analysis (RPCA) is to recover a low-rank matrix from a given data matrix. These problems gained great atten…