Final Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software

Linderoth, Jeff

Jeff Linderoth/Lehigh University

关键词: Mixed Integer Linear Programming;    Symmetry;    Mixed Integer Nonlinear Programming;    Computer Codes Numerical Optimization;    Optimization;   

DOI  :  10.2172/939366
RP-ID  :  DOE/ER/25694-1
RP-ID  :  FG02-05ER25694
RP-ID  :  939366

美国|英语

来源: UNT Digital Library

【 摘 要 】

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.