2 citations · 4 across the 8 of their papers we have counts for
8 papers · 1 filter
A Quadratic-Approximation-Based Stochastic Approximation Method for Weakly Convex Stochastic Programming
Yule Zhang, Benqi Liu, Xiantao Xiao +1
We propose a novel stochastic approximation algorithm, termed PMQSopt, for solving weakly convex stochastic optimization problems involving expectation-valued functions. The algori…
A Proximal Augmented Lagrangian Method Based on Quadratic Approximations for Weakly Convex Optimization
Yule Zhang, Benqi Liu, Xiantao Xiao +1
This paper proposes QPALM, a proximal augmented Lagrangian method based on quadratic approximations, for solving nonlinear programming problems with weakly convex objective and con…
Augmented Lagrangian Methods for Time-varying Constrained Online Convex Optimization
Haoyang Liu, Xiantao Xiao, Liwei Zhang
In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint functions. Specifically, the decision maker chooses sequential decisions based onl…
Regrets of Proximal Method of Multipliers for Online Non-convex Optimization with Long Term Constraints
Liwei Zhang, Haoyang Liu, Xiantao Xiao
The online optimization problem with non-convex loss functions over a closed convex set, coupled with a set of inequality (possibly non-convex) constraints is a challenging online…
Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm
Liwei Zhang, Yule Zhang, Jia Wu +1
This paper considers the problem of minimizing a convex expectation function with a set of inequality convex expectation constraints. We present a computable stochastic approximati…
Stochastic Approximation Proximal Method of Multipliers for Convex Stochastic Programming
Liwei Zhang, Yule Zhang, Jia Wu
This paper considers the problem of minimizing a convex expectation function over a closed convex set, coupled with a set of inequality convex expectation constraints. We present a…
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization
Andre Milzarek, Xiantao Xiao, Shicong Cen +2
In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the…
A Regularized Semi-Smooth Newton Method With Projection Steps for Composite Convex Programs
Xiantao Xiao, Yongfeng Li, Zaiwen Wen +1
The goal of this paper is to study approaches to bridge the gap between first-order and second-order type methods for composite convex programs. Our key observations are: i) Many w…