Blog post on the “IJKLM model” with AlgebraicJulia tools

[slwu89]

Hi all,

I recently wrote a blog post at the AlgebraicJulia blog: JuMP-ing with AlgebraicJulia I: The IJKLM model – AlgebraicJulia blog, which explores how to use some of the tools in the AlgebraicJulia ecosystem to address the relational algebra seen in the (infamous?) IJKLM model. AlgebraicJulia is a collection of packages which let users create data structures from applied category theory, and manipulate them categorically as well. I took inspiration from the excellent JuMP blog post at JuMP, GAMS, and the IJKLM model | JuMP. I hope this is of interest to some folks here.

I am currently writing a follow up blog post that looks more at what an AlgebraicJulia-powered workflow would look like on a practical model (including actually running JuMP.optimize! and visualization), specifically the network multi-commodity flow problem from the docs.

[slwu89]

I wrote a second blog post in this series, showing how to use ACSets (a categorical data structure generalizing both dataframes and graphs) to set up, solve, and visualize (see below!) the multi-commodity network problem from the JuMP docs here: JuMP-ing with AlgebraicJulia II: A practical optimization model – AlgebraicJulia blog

There is nearly zero applied category theory in this one, but if you are interested in ACT, please check out some of the other blogs at the AlgebraicJulia organization! If you’ve never heard of an ACSet (pronounced like hachet without the h), please feel invited to start with Graphs and C-sets I: What is a graph? – AlgebraicJulia blog