Fast Global Optimization for Partially-Separable Functions with Indicator Variables
Abstract
For mixed-integer linear optimization and local nonlinear optimization, over the last 50+ years, good modeling practices have become well known, and many important mathematical and algorithmic principles are now absorbed by solvers. For other important categories of optimization problems, mathematical and algorithmic theory and practice are much less developed. The area of mixed-integer nonlinear optimization is a broad and attractive paradigm to handle a wide variety of practical problems, with one main framework (spatial branch-andbound) for algorithms aimed at global optimization. We are developing a broad extension of the class of mathematical-optimization models that the main algorithmic framework can successfully attack. Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 06, 2021
- Source ID
- N000142112135
Entities
People
- Jon Lee
Organizations
- Board of Regents of the University of Michigan
- Office of Naval Research
- United States Navy