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

Linderoth, Jeff

University of Wisconsin--Madison

关键词: Numerical Optimization;    Mixed Integer Linear Programming;    Mixed Integer Nonlinear Programming;    97 Mathematics And Computing Numerical Optimization;   

DOI  :  10.2172/1028666
RP-ID  :  DOE/ER/25869
RP-ID  :  FG02-09ER25869
RP-ID  :  1028666

美国|英语

来源: UNT Digital Library

【 摘 要 】

the goal of this work 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. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.