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<h1 class="title">Operations Research and Optimisation: where to start?</h1>
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<p class="byline">April 8, 2020</p>
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<p><a href="https://en.wikipedia.org/wiki/Operations_research">Operations research</a> (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.</p>
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<p>Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.</p>
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<h1 id="why-is-it-hard-to-approach">Why is it hard to approach?</h1>
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<ul>
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<li>why it may be more difficult to approach than other, more recent areas like ML and DL
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<ul>
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<li>slightly longer history</li>
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<li>always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)</li>
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<li>often approached from a applied point of view means that many very different concepts are often mixed together</li>
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</ul></li>
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<li>why it is interesting and you should pursue it anyway
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<ul>
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<li>history of the field</li>
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<li>examples of applications</li>
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<li>theory perspective, rigorous field</li>
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</ul></li>
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<li>different subfields
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<ul>
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<li>optimisation: constrained and unconstrained</li>
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<li>game theory</li>
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<li>dynamic programming</li>
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<li>stochastic processes</li>
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<li>simulation</li>
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</ul></li>
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<li>how to learn and practice
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<ul>
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<li>references</li>
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<li>courses</li>
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<li>computational assets</li>
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