The difference between academia and industry when it comes to mathematical optimization?

🔵 Industry cares more about getting tangible results that lead to measurable improvements.
🟡 Academia often focuses on incremental advancements exploring theoretical limits.

🔵 In industry scalability is a critical factor as problems can be large.
🟡 In academia the impact of large constant factors is often overlooked in complexity analysis, focusing more on asymptotic behaviour.

🔵 Industry mostly uses MIPs or meta-heuristics to obtain solutions.
🟡 In academia the algorithms are often ad-hoc methods exploiting the specific structure of a given problem.

Underlying these varied approaches is a core difference in perspective:

🟡 Academia: The primary focus is to deeply analyse the complexity of a given problem, with the goal of developing multiple algorithms to understand the problem’s computational intricacies and limitations.

🔵 Industry: The reality is encountering a multitude of different problems, each with unique constraints and requirements. The need is for a more versatile algorithm – or a set of robust tools – capable of addressing this diverse landscape of use cases. Each distinct set of constraints effectively defines a new problem from an application standpoint.

This is why when talking to an academic you should ask what algorithms they specialise in, while with someone in industry you should ask what problems they focus on: in academia the problems are given, while in industry the algorithms are.

Ultimately, both academia and industry play vital, interconnected roles in the advancement of optimization. Neither can thrive in isolation, as they continuously learn from and build upon each other's contributions.

Academics might hold certain stereotypes about industry's lack of depth, while industry professionals might view academic research as too abstract or impractical.

However, these are merely two sides of the same coin in the broader field of computational problem-solving.

So be kind to your fellow peers on the other side of the divide, whether you are an academic or industry practitioner.

Peace! ☮️

🔵 If you work in industry, what do you value most about academia?
🟡 If you are an academic, what do you think is industry’s greatest strength?