Open AccessFinal Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software
Author(s) -
Jeff Linderoth
Publication year - 2008
Language(s) - English
Resource type - Reports
DOI - 10.2172/939366
Subject(s) - integer (computer science) , mathematical optimization , computer science , scale (ratio) , software , global optimization , algorithm , nonlinear system , optimization problem , state (computer science) , integer programming , mathematics , programming language , physics , quantum mechanics
The goal of this research was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems