[sinnsro]

While others have mentioned plenty of reasons, for this particular case I want to highlight 3 things:

1. Julia has great tooling for operations research/linear programming. JuMP provides an standardise interface to interact with solvers (e.g., Gurobi, CPLEX) via wrapper libraries.

2. I like its overall ergonomics. It is fast enough that a programmer might not need to use a compiled language for performance. The type system allows for multiple dispatch. And the syntax is more approachable than say Python for matrix algebra.

3. I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions. The two-language problem in academia is real, and Julia helps to reduce that gap somewhat in certain fields.

[SatvikBeri]

> might not need to use a compiled language

A very minor nit: Julia is a compiled language, but it has an unusual model where it compiles functions the first time they're used. This is why highly-optimized Julia can have pretty extreme performance.

> I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions.

For about a year we had a 2-hour problem in our hiring pipeline where the main goal was to write the fastest code possible to do a task, and the best 2 solutions were in Julia. C++ was a close third, and Rust after that.

[sinnsro]

> A very minor nit: Julia is a compiled language

Caught. Should have just listed the usual suspects (C, C++, maybe Rust nowadays?).

> and the best 2 solutions were in Julia. C++ was a close third, and Rust after that.

Awesome. Which type of problem was this, if you can share?