[philip1209]

I loved the JuMP package in Julia for being able to write models once, then swap in different solvers.

Most open-source solvers don't handle parallelization well, and they lack the latest research on techniques like branch-cutting and heuristics that can speed things up significantly.

In my experience, Gurobi is still leader for linear and MiP solving. But, it's really expensive and the licensing terms seem anachronistic.

SimpleRose.com was a startup working on a new solver, too - I'm curious if anybody has tried it yet?

[paulgb]

I love JuMP too. I am very amateur when it comes to optimization, but I found it to be a very efficient way to describe a matching algorithm for a post card exchange I ran (think of it as a Secret Santa, but generalized to each person sending multiple gifts, and discouraging mutual exchanges.) Here's the code: https://twitter.com/paulgb/status/1462483698427781120

To OP's question, besides JuMP, the other use case I've had for optimization is for optimizing the drawing order of pen plots, for which I used or-tools. A write-up on it is here: https://nb.paulbutler.org/optimizing-plots-with-tsp-solver/

[isatty]

> here’s the code

> link to twitter

visible_confusion.png.jpg.exe

[kqr]

I hadn't before realised that you can circumvent the Twitter character limit by turning your message into an image and attaching it. Clever!

[paulgb]

It’s funny, despite the code being on GitHub, Twitter search is the fastest way for me to find it. Searching my own tweets as a bookmarking tool is its hidden killer feature.

Images aren’t ideal for sharing code, of course, but it’s not like this example works as a standalone program.

[ryan-nextmv]

I haven't looked at JuMP in a while - the last time I tried it was when I was still doing personal blogging (https://ryanjoneil.github.io/posts/2014-07-18-are-we-getting...).

I remember liking JuMP, but Julia itself didn't feel ready yet. Some of the packages had weird behaviors. For example, Gadfly took several minutes to render some of my charts. IIRC when I looked at the source, it was solving a MIP to compute the best bounding box for display purposes.

I should probably check it out again.

Also: agreed regarding Gurobi licensing.

[Tarrosion]

Julia (and the Gadfly package, which it's worth noting is not a standard library or anything like that) have come a long way since 2014. JuMP too. Julia was indeed "not ready" in 2014; it wasn't anywhere near the 1.0 release. We're now on the 1.7.x releases: stable and guaranteed backward compatible since 1.0, and a lot of quality of life improvements between 1.0 and 1.7, including much faster compilation.

If I'm solving an optimization problem for personal curiosity, a blog post, etc., JuMP is the tool I reach for. :)

[ChrisRackauckas]

Wow, 2014 and Julia v0.2! That's pretty amazing really haha. That's the "before times". That's so long ago that most of the things tracking Julia's growth start tracking after that time, and the vast majority of users today hadn't even heard of Julia at that time. I would definitely open up Julia again some time treating it like essentially a wholly different language. Things like precompilation, package compiler, Plots.jl, etc. only came into being much later, so definitely OP should scratch the old workflow and try it fresh.

[SparseRunner]

HiGHS is now the default open-source LP/MIP solver in JuMP documentation. Performance-wise, for MIP HiGHS is way ahead of Cbc