8 papers
A Projection Anderson Acceleration Algorithm for Non-quasi-monotone Variational Inequalities with Hölder Continuous Terms
Xiaojun Chen, C. T. Kelley, Lei Wang
This paper proposes a Projection Anderson Acceleration (PAA) algorithm for variational inequality (VI) problems, where the underlying function is expressed as a finite sum of Hölde…
An Adaptive Projected-Gradient Algorithm for Sample-Average Approximations of Stochastic Multi-Objective Optimization
Yiyang Li, Lei Wang, Xiaojun Chen
We consider stochastic multi-objective optimization over a nonempty closed convex set, where every objective is an expectation and only sample-gradient information is available. We…
Stability of Differential Stochastic Variational Inequalities with History-Dependent Responses and Transfer Learning
Xiaojun Chen, Jian Guo, Xin Guo +1
In this paper, we propose and study a class of differential stochastic variational inequalities (DSVIs), in which an ordinary differential equation (ODE) is coupled with history-de…
Complexity of Projected Gradient Methods for Strongly Convex Optimization with Hölder Continuous Gradient Terms
Xiaojun Chen, C. T. Kelley, Lei Wang
This paper studies the complexity of projected gradient descent methods for a class of strongly convex constrained optimization problems where the objective function is expressed a…
Bayesian distributionally robust variational inequalities: regularization and quantification
Wentao Ma, Zhiping Chen, Xiaojun Chen
We propose a Bayesian distributionally robust variational inequality (DRVI) framework that models the data-generating distribution through a finite mixture family, which allows us…
Differential Stochastic Variational Inequalities with Parametric Optimization
Xiaojun Chen, Jian Guo, Guan Wang
The differential stochastic variational inequality with parametric convex optimization (DSVI-O) is an ordinary differential equation whose right-hand side involves a stochastic var…
A New Complexity Result for Strongly Convex Optimization with Locally -H{ö}lder Continuous Gradients
Xiaojun Chen, C. T. Kelley, Lei Wang
In this paper, we present a new complexity result for the gradient descent method with an appropriately fixed stepsize for minimizing a strongly convex function with locally -H{…
Robust Solutions of Nonlinear Least Squares Problems via Min-max Optimization
Xiaojun Chen, Carl Kelley
This paper considers robust solutions to a class of nonlinear least squares problems using min-max optimization approach. We give an explicit formula for the value function of the…