4 citations · 11 across the 13 of their papers we have counts for
13 papers
New Classes of Non-monotone Variational Inequality Problems Solvable via Proximal Gradient on Smooth Gap Functions
Lei Zhao, Daoli Zhu, Shuzhong Zhang
In this paper, we study the local linear convergence behavior of proximal-gradient (PG) descent algorithm on a parameterized gap-function reformulation of a smooth but non-monotone…
A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions
Daoli Zhu, Lei Zhao, Shuzhong Zhang
This paper presents a unified analysis for the proximal subgradient method (Prox-SubGrad) type approach to minimize an overall objective of , subject to convex constrain…
An Augmented Lagrangian Approach to Conically Constrained Non-monotone Variational Inequality Problems
Lei Zhao, Daoli Zhu, Shuzhong Zhang
In this paper we consider a non-monotone (mixed) variational inequality model with (nonlinear) convex conic constraints. Through developing an equivalent Lagrangian function-like p…
Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity
Xiao Li, Lei Zhao, Daoli Zhu +1
The subgradient method is one of the most fundamental algorithmic schemes for nonsmooth optimization. The existing complexity and convergence results for this method are mainly der…
Randomized Coordinate Subgradient Method for Nonsmooth Composite Optimization
Lei Zhao, Ding Chen, Daoli Zhu +1
Coordinate-type subgradient methods for addressing nonsmooth optimization problems are relatively underexplored due to the set-valued nature of the subdifferential. In this work, o…
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Daoli Zhu, Lei Zhao
Linear constrained convex programming has many practical applications, including support vector machine and machine learning portfolio problems. We propose the randomized primal-du…
Level-set Subdifferential Error Bounds and Linear Convergence of Variable Bregman Proximal Gradient Method
Daoli Zhu, Sien Deng, Minghua Li +1
In this work, we develop a level-set subdifferential error bound condition aiming towards convergence rate analysis of a variable Bregman proximal gradient (VBPG) method for a broa…
A First-Order Primal-Dual Method for Nonconvex Constrained Optimization Based On the Augmented Lagrangian
Daoli Zhu, Lei Zhao, Shuzhong Zhang
Nonlinearly constrained nonconvex and nonsmooth optimization models play an increasingly important role in machine learning, statistics and data analytics. In this paper, based on…
Linear Convergence of Variable Bregman Stochastic Coordinate Descent Method for Nonsmooth Nonconvex Optimization by Level-set Variational Analysis
Lei Zhao, Daoli Zhu
Large-scale nonconvex and nonsmooth problems have attracted considerable attention in the fields of compress sensing, big data optimization and machine learning. Exploring effectiv…
Stochastic Primal-Dual Coordinate Method with Large Step Size for Composite Optimization with Composite Cone-constraints
Daoli Zhu, Lei Zhao
We introduce a stochastic coordinate extension of the first-order primal-dual method studied by Cohen and Zhu (1984) and Zhao and Zhu (2018) to solve Composite Optimization with Co…
Stochastic Primal-Dual Coordinate Method for Nonlinear Convex Cone Programs
Daoli Zhu, Lei Zhao
Block coordinate descent (BCD) methods and their variants have been widely used in coping with large-scale nonconstrained optimization problems in many fields such as imaging proce…
First-Order Primal-Dual Method for Nonlinear Convex Cone Programming
Lei Zhao, Daoli Zhu
Nonlinear Convex Cone Programming (NCCP) problems are important and have many practical applications. In this paper, we introduces a flexible first-order primal-dual algorithm call…
Auxiliary Problem Principle of augmented Lagrangian with Varying Core Functions for Large-Scale Structured Convex Problems
Lei Zhao, Daoli Zhu, Bo Jiang
The auxiliary problem principle of augmented Lagrangian (APP-AL), proposed by Cohen and Zhu (1984), aims to find the solution of a constrained optimization problem through a sequen…