What should you know before using Julia for Operations Research?

1 Julia basics

Julia is a dynamic, multi-paradigm language that supports multiple dispatch, metaprogramming, and multiple inheritance. It also has a rich set of built-in data types, such as arrays, tuples, dictionaries, and sets. Julia has a simple syntax that is similar to Python, but also allows for low-level manipulation of memory and types. Julia is designed to be fast and efficient, using just-in-time (JIT) compilation and native code generation. Julia also has a powerful package manager that lets you install and update libraries and dependencies easily.

2 OR packages

Julia has an ever-expanding selection of packages for OR, encompassing various domains and methods. Among the most popular and helpful are JuMP, a modeling language and interface for optimization solvers; SimJulia, a discrete-event simulation framework; Distributions, a library with a comprehensive collection of probability distributions; and LightGraphs, a library with efficient data structures and algorithms for graph theory. JuMP offers features such as automatic differentiation, callbacks, and solver-independent syntax. SimJulia allows for the creation and execution of simulations in a coroutine-based style, and supports parallel and distributed operation. Distributions facilitates work with univariate and multivariate distributions, mixtures, transformations, and conjugacy. LightGraphs enables manipulation of graphs, traversal, clustering, centrality calculations, shortest path algorithms, and visualization of graphs using external tools.

3 OR challenges

Julia is a great language for OR, but it is also important to be aware of its challenges and limitations. The two-language problem refers to the need to use a high-level language for prototyping and a low-level language for performance, and in some cases, you may still need to use another language. Additionally, JIT compilation means that the first time you run a function or script, it will take longer than subsequent runs. Furthermore, Julia is an evolving language, which can lead to compatibility issues and breaking changes if different versions of Julia or packages are used. To avoid this, Project.toml and Manifest.toml files should be used to specify exact versions and dependencies of the project. Additionally, environments can help to isolate the project from the global Julia installation. While there are some challenges with using Julia for OR, by understanding its basics, packages, and challenges, you can effectively and efficiently use it for your projects and problems.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?