72 citations · 296 across the 47 of their papers we have counts for
52 papers
Complexity Guarantees for Zeroth-order Methods via Exponentially-shifted Gaussian Smoothing: Mitigating Dimension-dependence and Incorporating Decision-dependence
Mingrui Wang, Prakash Chakraborty, Uday V. Shanbhag
In this paper, we consider two distinct challenges in the resolution of nonsmooth stochastic optimization. Of these, the first pertains to the pronounced dependence of dimension in…
Equilibrium Invariance, Proximality, and Surrogation: Moreau-Smoothed Best-Response Pathways in Stochastic Nonsmooth Games
Zhuoyu Xiao, Uday V. Shanbhag
Best-response (BR) schemes represent an important avenue for learning equilibria in noncooperative games. However, extant rate guarantees for BR schemes generally necessitate strin…
A Stochastic LineseArch Method (SLAM) for Minimizing Expectation Residuals
Qi Wang, Uday V. Shanbhag, Yue Xie
Most existing rate and complexity guarantees for stochastic gradient methods in -smooth settings mandates that such sequences be non-adaptive, non-increasing, and upper bounded…
On the Sampling-based Computation of Nash Equilibria under Uncertainty via the Nikaido-Isoda Function
Luke Marrinan, Farzad Yousefian, Uday V. Shanbhag
We consider the computation of an equilibrium of a stochastic Nash equilibrium problem, where the player objectives are assumed to be -Lipschitz continuous and convex given ri…
Complexity guarantees for risk-neutral generalized Nash equilibrium problems
Haochen Tao, Andrea Iannelli, Meggie Marschner +3
In this paper, we address \ac{SGNEP} seeking with risk-neutral agents. Our main contribution lies the development of a stochastic variance-reduced gradient (SVRG) technique, modifi…
On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods
Mohammadjavad Ebrahimi, Uday V. Shanbhag, Farzad Yousefian
The mathematical program with equilibrium constraints (MPEC) is a powerful yet challenging class of constrained optimization problems, where the constraints are characterized by a…
Computing Equilibria in Stochastic Nonconvex and Non-monotone Games via Gradient-Response Schemes
Zhuoyu Xiao, Uday V. Shanbhag
We consider a class of smooth -player noncooperative games, where players' objectives are expectation-valued and potentially nonconvex. In such a setting, we consider the largel…
Zeroth-order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization
Luke Marrinan, Uday V. Shanbhag, Farzad Yousefian
We consider the minimization of a Lipschitz continuous and expectation-valued function, denoted by and defined as $f(\mathbf{x}) \triangleq \mathbb{E}[\tilde{f}(\mathbf{x}, \ma…
Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs
Mohammadjavad Ebrahimi, Uday V. Shanbhag, Farzad Yousefian
We consider a class of hierarchical multi-agent optimization problems over networks where agents seek to compute an approximate solution to a single-stage stochastic mathematical p…
Zeroth-Order Federated Methods for Stochastic MPECs and Nondifferentiable Nonconvex Hierarchical Optimization
Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian
Motivated by the emergence of federated learning (FL), we design and analyze federated methods for addressing: (i) Nondifferentiable nonconvex optimization; (ii) Bilevel optimizati…
A Distributed Iterative Tikhonov Method for Networked Monotone Aggregative Hierarchical Stochastic Games
Jinlong Lei, Uday V. Shanbhag, Jie Chen
We consider a class of nonsmooth aggregative games over networks in stochastic regimes, where each player is characterized by a composite cost function , is a smooth…
A regularized variance-reduced modified extragradient method for stochastic hierarchical games
Shisheng Cui, Uday V. Shanbhag, Mathias Staudigl
We consider an N-player hierarchical game in which the i-th player's objective comprises of an expectation-valued term, parametrized by rival decisions, and a hierarchical term. Su…
No-regret distributed learning in subnetwork zero-sum games
Shijie Huang, Jinlong Lei, Yiguang Hong +2
In this paper, we consider a distributed learning problem in a subnetwork zero-sum game, where agents are competing in different subnetworks. These agents are connected through tim…
Stochastic Relaxed Inertial Forward-Backward-Forward splitting for Monotone Inclusions in Hilbert spaces
Shisheng Cui, Uday V. Shanbhag, Mathias Staudigl +1
We consider monotone inclusions defined on a Hilbert space where the operator is given by the sum of a maximal monotone operator and a single-valued monotone, Lipschitz continu…
Zeroth-order randomized block methods for constrained minimization of expectation-valued Lipschitz continuous functions
Uday V. Shanbhag, Farzad Yousefian
We consider the minimization of an -Lipschitz continuous and expectation-valued function, denoted by and defined as , over a Car…
Complexity guarantees for an implicit smoothing-enabled method for stochastic MPECs
Shisheng Cui, Uday V. Shanbhag, Farzad Yousefian
Stochastic MPECs have found increasing relevance for modeling a broad range of settings in engineering and statistics. Yet, there seem to be no efficient first/zeroth-order schemes…
On the computation of equilibria in monotone and potential stochastic hierarchical games
Shisheng Cui, Uday V. Shanbhag
We consider a class of hierarchical noncooperative -player games where the th player solves a parametrized stochastic mathematical program with equilibrium constraints (MPEC)…
A relaxed-inertial forward-backward-forward algorithm for Stochastic Generalized Nash equilibrium seeking
Shisheng Cui, Barbara Franci, Sergio Grammatico +2
In this paper we propose a new operator splitting algorithm for distributed Nash equilibrium seeking under stochastic uncertainty, featuring relaxation and inertial effects. Our wo…
Variance-Reduced Splitting Schemes for Monotone Stochastic Generalized Equations
Shisheng Cui, Uday V. Shanbhag
We consider monotone inclusion problems where the operators may be expectation-valued, a class of problems that subsumes convex stochastic optimization problems as well as subclass…
Variance-Reduced Accelerated First-order Methods: Central Limit Theorems and Confidence Statements
Jinlong Lei, Uday V. Shanbhag
In this paper, we study a stochastic strongly convex optimization problem and propose three classes of variable sample-size stochastic first-order methods including the standard st…
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…
On the analysis of variance-reduced and randomized projection variants of single projection schemes for monotone stochastic variational inequality problems
Shisheng Cui, Uday V. Shanbhag
Classical extragradient schemes and their stochastic counterpart represent a cornerstone for resolving monotone variational inequality problems. Yet, such schemes have a per-iterat…
Distributed Variable Sample-Size Gradient-response and Best-response Schemes for Stochastic Nash Equilibrium Problems over Graphs
Jinlong Lei, Uday V. Shanbhag
This paper considers a stochastic Nash game in which each player minimizes an expectation valued composite objective. We make the following contributions. (I) Under suitable monoto…
Linearly Convergent Variable Sample-Size Schemes for Stochastic Nash Games: Best-Response Schemes and Distributed Gradient-Response Schemes
Jinlong Lei, Uday V. Shanbhag
This paper considers an -player stochastic Nash game in which the th player minimizes a composite objective , where is expectation-valued and h…