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.
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.