# Contributing¶

We’d welcome contributions to the Convex.jl package. Here are some short instructions on how to get started. If you don’t know what you’d like to contribute, you could

- take a look at the current issues and pick one. (Feature requests are probably the easiest to tackle.)
- add a usage example.

Then submit a pull request (PR). (Let us know if it’s a work in progress by putting [WIP] in the name of the PR.)

## Adding examples¶

- Take a look at our exising usage examples and add another in similar style.
- Submit a PR. (Let us know if it’s a work in progress by putting [WIP] in the name of the PR.)
- We’ll look it over, fix up anything that doesn’t work, and merge it!

## Adding atoms¶

Here are the steps to add a new function or operation (atom) to Convex.jl. Let’s say you’re adding the new function \(f\).

- Take a look at the nuclear norm atom for an example of how to construct atoms, and see the norm atom for an example of an atom that depends on a parameter.
- Copy paste (eg) the nuclear norm file, replace anything saying nuclear norm with the name of the atom \(f\), fill in monotonicity, curvature, etc. Save it in the appropriate subfolder of
`src/atoms/`

.- Add as a comment a description of what the atom does and its parameters.
- The most mathematically interesting part is the
`conic_form!`

function. Following the example in the nuclear norm atom, you’ll see that you can just construct the problem whose optimal value is \(f(x)\), introducing any auxiliary variables you need, exactly as you would normally in Convex.jl, and then call`cache_conic_form!`

on that problem.- Add a test for the atom so we can verify it works in
`test/test_<cone>`

, where`<cone>`

matches the subfolder of`src/atoms`

.- Submit a PR, including a description of what the atom does and its parameters. (Let us know if it’s a work in progress by putting [WIP] in the name of the PR.)
- We’ll look it over, fix up anything that doesn’t work, and merge it!

## Fixing the guts¶

If you want to do a more major bug fix, you may need to understand how Convex.jl thinks about conic form. To do this, start by reading the Convex.jl paper. You may find our JuliaCon 2014 talk helpful as well; you can find the ipython notebook presented in the talk here.

Then read the conic form code:

- We define data structures for conic objectives and conic constraints, and simple ways of combining them, in conic_form.jl
- We convert the internal conic form representation into the standard form for conic solvers in the function conic_problem.
- We solve problems (that is, pass the standard form of the problem to a solver, and put the solution back into the values of the appropriate variables) in solution.jl.

You’re now armed and dangerous. Go ahead and open an issue (or comment on a previous one) if you can’t figure something out, or submit a PR if you can figure it out. (Let us know if it’s a work in progress by putting [WIP] in the name of the PR.)

PRs that comment the code more thoroughly will also be welcomed.