activity
20182024
most citedA Decomposition Method for Distributionally-Robust Two-stage Stochastic Mixed-integer Cone Programs

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

math.OC2024

Structured Nonsmooth Optimization Using Functional Encoding and Branching Information

Fengqiao Luo

We develop a novel gradient-based algorithm for optimizing nonsmooth nonconvex functions where nonsmoothness arises from explicit nonsmooth operators in the objective's analytical…

math.OC2020

A Distributionally-Robust Service Center Location Problem with Decision Dependent Demand Induced from a Maximum Attraction Principle

Fengqiao Luo

We establish and analyze a service center location model with a simple but novel decision-dependent demand induced from a maximum attraction principle. The model formulations are i…

math.OC2020

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…

math.OC2019

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…

math.OC20191 cited

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…

math.OC2018

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…