Abstract
At the dawn of optimization (the nineteen fifties), the state-of-the-art was defined by linear optimization models and the simplex method, the only reasonably efficient algorithm known at the time to solve such models. When I started studying this subject, one repeatedly heard from multiple sources that over 70% of the CPU cycles in the world were devoted to running various simplex codes. Surely an exaggeration, but it is indicative of the power of linear models. The world is not linear, but sometimes a linear approximation is good enough.
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Notes
- 1.
I add this temporal precision on the odd chance that this text is still being read long after my body has maximized its entropy.
- 2.
To encourage the reader to experiment, every model in this book is available in the additional material ( https://github.com/sgkruk/Apress-AI ), along with a random instance generator.
- 3.
Mostly to make the code fit a page, but also to hide some of the verbosity of the OR-Tools library. The authors chose, rightly in my opinion, meaningful but rather long names for their functions.
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© 2018 Serge Kruk
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Kruk, S. (2018). Linear Continuous Models. In: Practical Python AI Projects. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3423-5_2
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DOI: https://doi.org/10.1007/978-1-4842-3423-5_2
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Publisher Name: Apress, Berkeley, CA
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Online ISBN: 978-1-4842-3423-5
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