Abstract Models and Their Solution

Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson and David L. Woodruff
Additional contact information
Michael L. Bynum: Sandia National Laboratories
Gabriel A. Hackebeil: Deepfield Nokia
William E. Hart: Sandia National Laboratories
Carl D. Laird: Sandia National Laboratories
Bethany L. Nicholson: Sandia National Laboratories
John D. Siirola: Sandia National Laboratories
Jean-Paul Watson: Lawrence Livermore National Laboratory
David L. Woodruff: University of California

Chapter Chapter 10 in Pyomo — Optimization Modeling in Python, 2021, pp 137-168 from Springer

Abstract: Abstract This chapter describes how to declare and use an AbstractModel and data command files to initilize abstract models. Finally, this chapter describes the pyomo command, which makes it particularly easy to solve an abstract model using data command files. Although concrete and abstract models provide similar functionality, abstract models make a strong seperation of model formulation and model data, which is conceptually nice and practically useful in some contexts.

Date: 2021
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:978-3-030-68928-5_10

Ordering information: This item can be ordered from
http://www.springer.com/9783030689285

DOI: 10.1007/978-3-030-68928-5_10

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 ().