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