200 citations · 244 across the 14 of their papers we have counts for
15 papers
On the Relevance of Incorporating Decision Dependence in Distributional Ambiguity
Hamed Rahimian, Sanjay Mehrotra
Most existing studies on distributionally robust optimization (DRO) with a decision-dependent ambiguity set focus on the computational and theoretical challenges posed by this clas…
Diversity Subsampling: Custom Subsamples from Large Data Sets
Boyang Shang, Daniel W. Apley, Sanjay Mehrotra
Subsampling from a large data set is useful in many supervised learning contexts to provide a global view of the data based on only a fraction of the observations. Diverse (or spac…
A New Method for Computing Stationary Distribution and Steady-State Performance Measures of a Continuous-State Markov Chain with a Queuing Application
Shukai Li, Sanjay Mehrotra
Applications of stochastic models often involve the evaluation of steady-state performance, which requires solving a set of balance equations. In most cases of interest, the number…
A Cutting-plane and Benders' Decomposition Algorithm for Two-Stage Distributionally Robust Convex programs
Fengqiao Luo, Shibshankar Dey, Sanjay Mehrotra
We present a finitely convergent cutting-plane algorithm for solving a general mixed-integer convex program given an oracle for solving a general convex program. This method is ext…
A Model of Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19
Sanjay Mehrotra, Hamed Rahimian, Masoud Barah +2
This paper presents a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different t…
A Geometric Branch and Bound Method for a Class of Robust Maximization Problems of Convex Functions
Fengqiao Luo, Sanjay Mehrotra
We investigate robust optimization problems defined for maximizing convex functions. For finite uncertainty set, we develop a geometric branch-and-bound algorithmic approach to sol…
A Decomposition Method for Distributionally-Robust Two-stage Stochastic Mixed-integer Cone Programs
Fengqiao Luo, Sanjay Mehrotra
We develop a decomposition algorithm for distributionally-robust two-stage stochastic mixed-integer convex cone programs, and its important special case of distributionally-robust…
Service Center Location with Decision Dependent Utilities with an Application to Early Stage Testing and Vaccination in Epidemic Planning
Fengqiao Luo, Sanjay Mehrotra
We study a service center location problem with ambiguous utility gains upon receiving service. The model is motivated by the problem of deciding medical clinic/service centers, po…
Distributionally Robust Optimization: A Review
Hamed Rahimian, Sanjay Mehrotra
The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also w…
Distributionally Robust Optimization with Decision Dependent Ambiguity Sets
Fengqiao Luo, Sanjay Mehrotra
We study decision dependent distributionally robust optimization models, where the ambiguity sets of probability distributions can depend on the decision variables. These models ar…
A Concentration Result of Estimating Phi-Divergence using Data Dependent Partition
Fengqiao Luo, Sanjay Mehrotra
Estimation of the -divergence between two unknown probability distributions using empirical data is a fundamental problem in information theory and statistical learning. We cons…
Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metric
Fengqiao Luo, Sanjay Mehrotra
We study distributionally robust optimization (DRO) problems where the ambiguity set is defined using the Wasserstein metric. We show that this class of DRO problems can be reformu…
Designed Sampling from Large Databases for Controlled Trials
Liwen Ouyang, Daniel W. Apley, Sanjay Mehrotra
The increasing prevalence of rich sources of data and the availability of electronic medical record databases and electronic registries opens tremendous opportunities for enhancing…
A cutting surface algorithm for semi-infinite convex programming with an application to moment robust optimization
Sanjay Mehrotra, David Papp
We present and analyze a central cutting surface algorithm for general semi-infinite convex optimization problems, and use it to develop a novel algorithm for distributionally robu…
Generating nested quadrature formulas for general weight functions with known moments
Sanjay Mehrotra, Dávid Papp
We revisit the problem of extending quadrature formulas for general weight functions, and provide a generalization of Patterson's method for the constant weight function. The metho…