Large Scale Optimization
Abstract
Approved for Public Release Motivated by first-hand, real-world implementation of optimization models within DoD, the proposed project aims to extend prior ONR-funded research in large-scale optimization by developing novel models, approaches, and algorithms in the interface between mathematical optimization and machine learning. Leveraging the interplay between the two areas, we focus on (1)constructing decision rules in the context of complex DoD applications, (2) learning effective model restrictions to improve solution times in integer programs, (3) assessing and promoting robust decisions, (4) modeling uncertainty, and (5) quantifying the value of information. Across these thrusts, the proposed research aims to support, refine, and expand legacy models and new models.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412741
Entities
People
- Johannes Royset
Organizations
- Office of Naval Research
- United States Navy
- University of Southern California