Introduction

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 1 in Pyomo — Optimization Modeling in Python, 2021, pp 1-11 from Springer

Abstract: Abstract Abstract This chapter introduces and motivates Pyomo, a Python-based tool for modeling and solving optimization problems. Modeling is a fundamental process in many aspects of scientific research, engineering, and business. Algebraic modeling languages like Pyomo are high-level languages for specifying and solving mathematical optimization problems. Pyomo is a flexible, extensible modeling framework that captures and extends central ideas found in modern algebraic modeling languages, all within the context of a widely used programming language.

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_1

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

DOI: 10.1007/978-3-030-68928-5_1

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 ().