Bilevel Programming

William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson and John D. Siirola
Additional contact information
William E. Hart: Sandia National Laboratories
Carl D. Laird: Sandia National Laboratories
Jean-Paul Watson: Sandia National Laboratories
David L. Woodruff: University of California, Davis
Gabriel A. Hackebeil: University of Michigan
Bethany L. Nicholson: Sandia National Laboratories
John D. Siirola: Sandia National Laboratories

Chapter Chapter 13 in Pyomo — Optimization Modeling in Python, 2017, pp 223-233 from Springer

Abstract: Abstract This chapter documents how to formulate bilevel programs, which model adversarial behavior in a general manner. We describe new modeling components that represent subproblems, modeling transformations for re-expressing models with bilevel structure in other forms, and optimize bilevel programs with metasolvers that apply transformations and then perform optimization on the resulting model. We illustrate the breadth of Pyomo’s modeling capabilities for bilevel programs, and we describe how Pyomo’s meta-solvers can perform local and global optimization of bilevel programs.

Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-58821-6_13

Ordering information: This item can be ordered from
http://www.springer.com/9783319588216

DOI: 10.1007/978-3-319-58821-6_13

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().