60 lines
2.7 KiB
Org Mode
60 lines
2.7 KiB
Org Mode
---
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title: "Operations Research and Optimisation: where to start?"
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date: 2020-04-08
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---
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[[https://en.wikipedia.org/wiki/Operations_research][Operations research]] (OR) is a vast area comprising a lot of theory,
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different branches of mathematics, and too many applications to
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count. In this post, I will try to explain why I find it so
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fascinating, but also why it can be a little disconcerting to explore
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at first. Then I will try to ease the newcomer's path in this rich
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area, by suggesting a very rough "map" of the field and a few
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references to get started.
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Keep in mind that although I studied it during my graduate studies,
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this is not my primary area of expertise (I'm a data scientist by
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trade), and I definitely don't pretend to know everything in OR. This
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is a field too vast for any single person to understand in its
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entirety, and I talk mostly from a "amateur mathematician and computer
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scientist" standpoint.
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* Why is it hard to approach?
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Operations research can be difficult to approach, since there are many
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references and subfields. Compared to machine learning for instance,
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OR has a slightly longer history (going back to the 17th century, for
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example with Monge and the optimal transport problem). This means that
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good textbooks and such have existed for a long time, but also that
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there will be plenty of material to choose from.
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Moreover, OR is very close to applications. Sometimes methods may vary
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a lot in their presentation depending on whether they're applied to
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train tracks, sudoku, or travelling salesmen. In practice, the
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terminology and notations are not the same everywhere. This is
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disconcerting if you are used to mathematics, where notations evolved
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over a long time and is pretty much standardised for many areas. In
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contrast, if you're used to the statistics literature with its [[https://lingpipe-blog.com/2009/10/13/whats-wrong-with-probability-notation/][strange
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notations]], you will find that OR is actually very well formalised.
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- why it may be more difficult to approach than other, more recent
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areas like ML and DL
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- slightly longer history
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- always very close to applications: somehow more "messy" in its
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notations, vocabulary, standard references, etc, as other "purer"
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fields of maths (similar to stats in this regard)
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- often approached from a applied point of view means that many very
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different concepts are often mixed together
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- why it is interesting and you should pursue it anyway
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- history of the field
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- examples of applications
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- theory perspective, rigorous field
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- different subfields
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- optimisation: constrained and unconstrained
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- game theory
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- dynamic programming
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- stochastic processes
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- simulation
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- how to learn and practice
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- references
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- courses
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- computational assets
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