Performance: Model Construction and Solver Interfaces

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
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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 9 in Pyomo — Optimization Modeling in Python, 2021, pp 123-135 from Springer

Abstract: Abstract This chapter documents tools for profiling model construction and improving the performance of both model construction and interaction with solvers. We begin by discussing various profiling tools which can be used to help identify performance bottlenecks. Pyomo has built-in profiling capabilities, but there are also Python packages, such as cProfile and line profiler, dedicated to performance profiling. Section 9.2 discusses the LinearExpression class, which can be used to drastically improve model construction time for some applications. Section 9.3 describes how persistent solver interfaces can be used to repeatedly solve models with small changes very efficiently. Finally, Section 9.4 discusses sparse index sets.

Date: 2021
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DOI: 10.1007/978-3-030-68928-5_9

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