---
title: "Operations Research and Optimisation: where to start?"
date: 2020-04-08
---

[[https://en.wikipedia.org/wiki/Operations_research][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?

- 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
- why it is interesting and you should pursue it anyway
  - history of the field
  - examples of applications
  - theory perspective, rigorous field
- different subfields
  - optimisation: constrained and unconstrained
  - game theory
  - dynamic programming
  - stochastic processes
  - simulation
- how to learn and practice
  - references
  - courses
  - computational assets