Abstract
Artificial intelligence is a wide field covering diverse techniques, objectives, and measures of success. One branch is concerned with finding provably optimal solutions to some well-defined problems.
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Notes
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I use the term “optimizers ” to name the mathematicians, theoreticians, and practitioners, who, since the nineteen-fifties, have worked in the fields of linear programming (LP) and integer programming (IP). There are others who could make valid claims to the moniker, chiefly among them researchers in constraint programming, but my focus will be mostly in LP and IP models, hence my restricted definition.
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My doctoral advisor used to say “There are error-free mathematical papers.” But we only have found an existence proof of that theorem. I will not claim that the code is error-free, but I am certain that it has fewer errors than any mathematical paper I ever wrote.
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The one and only editor: http://emacs.org
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This seemingly trivial change from “exactly equal” to “at most” represents more than 2,000 years of mathematical development in solution techniques. We have known how to solve the “equal” form since ancient Babylonians (though it is known today as “Gaussian elimination”) and we teach it in high school, but we only discovered how to solve the “at most” form in the twentieth century.
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Writing in Common Lisp would be even better. Alas, there is no Lisp binding for OR-Tools yet.
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See, for example, Alexander Schrijver, Theory of Linear and Integer Programming (Hoboken, NJ: Wiley, 1998).
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© 2018 Serge Kruk
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Kruk, S. (2018). Introduction. In: Practical Python AI Projects. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3423-5_1
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DOI: https://doi.org/10.1007/978-1-4842-3423-5_1
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