Nonlinear Programming with Pyomo
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 7 in Pyomo — Optimization Modeling in Python, 2017, pp 121-143 from Springer
Abstract:
Abstract This chapter describes the nonlinear programming capabilities of Pyomo. It presents the nonlinear expressions and functions that are supported, and it provides some tips for formulating and solving nonlinear programming problems. Pyomo makes use of the interface provided by the AMPL Solver Library to provide efficient expression evaluation and automatic differentiation. Use of the AMPL Solver Library means that any AMPL-enabled solver should be usable as a solver within the Pyomo framework. This chapter also provides several real-world examples to illustrate formulating and solving nonlinear programming problems.
Keywords: Nonlinear Programming; Continuously Stir Tank Reactor; Nonlinear Programming Problem; Deer Population; Outlet Concentration (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-58821-6_7
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DOI: 10.1007/978-3-319-58821-6_7
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