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Nonlinear Programming with Pyomo

In: Pyomo – Optimization Modeling in Python

Author

Listed:
  • William E. Hart

    (Sandia National Laboratories)

  • Carl Laird

    (Texas A&M)

  • Jean-Paul Watson

    (Sandia National Laboratories)

  • David L. Woodruff

    (University of California, Davis)

Abstract

The modeling components that are described earlier in the book can be used within nonlinear models. This chapter describes the nonlinear programming capabilities of Pyomo. It presents all 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.

Suggested Citation

  • William E. Hart & Carl Laird & Jean-Paul Watson & David L. Woodruff, 2012. "Nonlinear Programming with Pyomo," Springer Optimization and Its Applications, in: Pyomo – Optimization Modeling in Python, edition 127, chapter 0, pages 105-129, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-3226-5_8
    DOI: 10.1007/978-1-4614-3226-5_8
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