220 citations · 251 across the 24 of their papers we have counts for
19 papers · 1 filter
Distributionally Robust Optimization via Targeted Integral Probability Metrics for General Data Processes
Lanran Fang, Jianqiang Cheng, Grani A. Hanasusanto +1
Distributionally robust optimization (DRO) provides a principled framework for decision-making under distributional uncertainty. Classical data-driven DRO frameworks typically cons…
Data-Driven Contextual Optimization with Gaussian Mixtures: Flow-Based Generalization, Robust Models, and Multistage Extensions
YoungChul Yoon, Ling Dai, Grani A. Hanasusanto +2
Contextual optimization enhances decision quality by leveraging side information to improve predictions of uncertain parameters. However, existing approaches face significant chall…
A Distributionally Robust Optimization Approach to Quick Response Models under Demand Uncertainty
Panayotis P. Papavassilopoulos, Grani A. Hanasusanto, Yijie Wang
Quick response is a widely adopted strategy to mitigate overproduction in the manufacturing industry, yet recent research reveals a counter-intuitive paradox: while it reduces wast…
Sample Complexity of Data-driven Multistage Stochastic Programming under Markovian Uncertainty
Hyuk Park, Grani A. Hanasusanto
This work is motivated by the challenges of applying the sample average approximation (SAA) method to multistage stochastic programming with an unknown continuous-state Markov proc…
Generalization Bounds for Contextual Stochastic Optimization using Kernel Regression
Yijie Wang, Grani A. Hanasusanto, Chin Pang Ho
In this paper, we consider contextual stochastic optimization using Nadaraya-Watson kernel regression, which is one of the most common approaches in nonparametric regression. Recen…
Distributionally Robust Performative Optimization
Zhuangzhuang Jia, Yijie Wang, Roy Dong +1
In performative stochastic optimization, decisions can influence the distribution of random parameters, rendering the data-generating process itself decision-dependent. In practice…
Distributionally Robust Optimization with Decision-Dependent Information Discovery
Qing Jin, Angelos Georghiou, Phebe Vayanos +1
We study two-stage distributionally robust optimization (DRO) problems with decision-dependent information discovery (DDID) wherein (a portion of) the uncertain parameters are reve…
Distributionally Fair Stochastic Optimization using Wasserstein Distance
Qing Ye, Grani A. Hanasusanto, Weijun Xie
A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a…
Distributionally Robust Observable Strategic Queues
Yijie Wang, Madhushini Narayana Prasad, Grani A. Hanasusanto +1
This paper presents an extension of Naor's analysis on the join-or-balk problem in observable M/M/1 queues. While all other Markovian assumptions still hold, we explore this proble…
On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach
Prateek R. Srivastava, Yijie Wang, Grani A. Hanasusanto +1
We consider generic stochastic optimization problems in the presence of side information which enables a more insightful decision. The side information constitutes observable exoge…
A Decision Rule Approach for Two-Stage Data-Driven Distributionally Robust Optimization Problems with Random Recourse
Xiangyi Fan, Grani A. Hanasusanto
We study two-stage stochastic optimization problems with random recourse, where the adaptive decisions are multiplied with the uncertain parameters in both the objective function a…
Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration
Yuqi Zhou, Hao Zhu, Grani A. Hanasusanto
Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a…
Transmission Switching Under Wind Uncertainty Using Linear Decision Rules
Yuqi Zhou, Hao Zhu, Grani A. Hanasusanto
Increasing penetration of wind and renewable generation poses significant challenges to the power system operations and reliability. This paper considers the real-time optimal tran…
Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming
Guanglin Xu, Grani A. Hanasusanto
We study decision rule approximations for generic multi-stage robust linear optimization problems. We consider linear decision rules for the case when the objective coefficients, t…
Finding Minimum Volume Circumscribing Ellipsoids Using Generalized Copositive Programming
Areesh Mittal, Grani A. Hanasusanto
We study the problem of finding the Lowner-John ellipsoid, i.e., an ellipsoid with minimum volume that contains a given convex set. We reformulate the problem as a generalized copo…
Improved Conic Reformulations for K-means Clustering
Madhushini Narayana Prasad, Grani A. Hanasusanto
In this paper, we show that the popular K-means clustering problem can equivalently be reformulated as a conic program of polynomial size. The arising convex optimization problem i…
Robust Quadratic Programming with Mixed-Integer Uncertainty
Areesh Mittal, Can Gokalp, Grani A. Hanasusanto
We study robust convex quadratic programs where the uncertain problem parameters can contain both continuous and integer components. Under the natural boundedness assumption on the…
Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
Grani A. Hanasusanto, Daniel Kuhn
Adaptive robust optimization problems are usually solved approximately by restricting the adaptive decisions to simple parametric decision rules. However, the corresponding approxi…
Data-driven Inverse Optimization with Imperfect Information
Peyman Mohajerin Esfahani, Soroosh Shafieezadeh-Abadeh, Grani Adiwena Hanasusanto +1
In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the o…