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— title: "Operations Research and Optimisation: where to start?" date: 2020-04-08 —
Operations research (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.
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.
Why is it hard to approach?
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why it may be more difficult to approach than other, more recent areas like ML and DL
- slightly longer history
- 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)
- often approached from a applied point of view means that many very different concepts are often mixed together
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why it is interesting and you should pursue it anyway
- history of the field
- examples of applications
- theory perspective, rigorous field
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different subfields
- optimisation: constrained and unconstrained
- game theory
- dynamic programming
- stochastic processes
- simulation
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how to learn and practice
- references
- courses
- computational assets