20 citations · 29 across the 10 of their papers we have counts for
13 papers
Recursive McCormick Linearization of Multilinear Programs
Arvind U Raghunathan, Carlos Cardonha, David Bergman +1
Linear programming (LP) relaxations are widely employed in exact solution methods for multilinear programs (MLP). One example is the family of Recursive McCormick Linearization (RM…
Constraint Learning to Define Trust Regions in Predictive-Model Embedded Optimization
Chenbo Shi, Mohsen Emadikhiav, Leonardo Lozano +1
There is a recent proliferation of research on the integration of machine learning and optimization. One expansive area within this research stream is predictive-model embedded opt…
Optimizing the expected maximum of two linear functions defined on a multivariate Gaussian distribution
David Bergman, Carlos Cardonha, Jason Imbrogno +1
We study stochastic optimization problems with objective function given by the expectation of the maximum of two linear functions defined on the component random variables of a mul…
Optimizing over an ensemble of neural networks
Keliang Wang, Leonardo Lozano, Carlos Cardonha +1
We study optimization problems where the objective function is modeled through feedforward neural networks with rectified linear unit (ReLU) activation. Recent literature has explo…
JANOS: An Integrated Predictive and Prescriptive Modeling Framework
David Bergman, Teng Huang, Philip Brooks +2
Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling framework JANOS that seamlessly integra…
Template-based Minor Embedding for Adiabatic Quantum Optimization
Thiago Serra, Teng Huang, Arvind Raghunathan +1
Quantum Annealing (QA) can be used to quickly obtain near-optimal solutions for Quadratic Unconstrained Binary Optimization (QUBO) problems. In QA hardware, each decision variable…