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Practical Computational Optimization Using Python

In: Practical Mathematical Optimization

Author

Listed:
  • Jan A. Snyman

    (University of Pretoria)

  • Daniel N. Wilke

    (University of Pretoria)

Abstract

Python is a general purpose computer programming language. An experienced programmer in any procedural computer language can learn Python very quickly. Python is remarkable in that it is designed to allow new programmers to efficiently master programming. The choice of including Anaconda Python for application of our mathematical programming concepts is motivated by the fact that Anaconda Python supports both symbolic and numerical mathematical operations as part of the installation. Python allows for an intuitive engagement with numerical computations. It is freely available and allows for additional functionality to be developed and extended. All algorithms in this text are made available in Python so as to allow the reader the use of the developed algorithms from the onset. This chapter is not an exhaustive treatise on Python and programming in general, but rather the minimum subset of Python required to implement formulated optimization problems and to solve them.

Suggested Citation

  • Jan A. Snyman & Daniel N. Wilke, 2018. "Practical Computational Optimization Using Python," Springer Optimization and Its Applications, in: Practical Mathematical Optimization, edition 2, chapter 0, pages 311-340, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-77586-9_9
    DOI: 10.1007/978-3-319-77586-9_9
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