5 papers
Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems
Morteza Boroun, Afrooz Jalilzadeh
Stochastic nonconvex optimization problems with nonlinear constraints have a broad range of applications in intelligent transportation, cyber-security, and smart grids. In this pap…
A Stochastic Variance-reduced Accelerated Primal-dual Method for Finite-sum Saddle-point Problems
Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh
In this paper, we propose a variance-reduced primal-dual algorithm with Bregman distance for solving convex-concave saddle-point problems with finite-sum structure and nonbilinear…
Primal-Dual Incremental Gradient Method for Nonsmooth and Convex Optimization Problems
Afrooz Jalilzadeh
In this paper, we consider a nonsmooth convex finite-sum problem with a conic constraint. To overcome the challenge of projecting onto the constraint set and computing the full (su…
A Proximal-Point Algorithm with Variable Sample-sizes (PPAWSS) for Monotone Stochastic Variational Inequality Problems
Afrooz Jalilzadeh, Uday V. Shanbhag
We consider a stochastic variational inequality (SVI) problem with a continuous and monotone mapping over a closed and convex set. In strongly monotone regimes, we present a variab…
A Variable Sample-size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization
Afrooz Jalilzadeh, Angelia Nedich, Uday V. Shanbhag +1
Classical theory for quasi-Newton schemes has focused on smooth deterministic unconstrained optimization while recent forays into stochastic convex optimization have largely reside…