# ProximalOperators.jl
**URL:** https://discourse.julialang.org/t/proximaloperators-jl/1064
**Category:** Optimization (Mathematical)
**Tags:** package, announcement
**Created:** [December 20, 2016, 4:37pm UTC](https://discourse.julialang.org/t/proximaloperators-jl/1064 "2016-12-20T16:37:45Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@lostella](https://discourse.julialang.org/u/lostella)
#### Post date: [December 20, 2016, 4:37pm UTC](https://discourse.julialang.org/t/proximaloperators-jl/1064/1 "2016-12-20T16:37:45Z")
Hi all,
We registered our package [ProximalOperators.jl](https://github.com/kul-forbes/ProximalOperators.jl), containing the implementation of the proximal mapping associated with several convex and nonconvex penalty functions, indicator functions and so on. This can be useful to quickly implement prox-based algorithms, such as ADMM, the proximal gradient method, or the Vu-Condat primal-dual splitting algorithm.
We tried to keep every implementation of prox as efficient as possible, but I’m sure that there’s a lot of room for improvement.
Please don’t hesitate to give your feedback and suggest additional features or prox’s that may be missing!
Best,
Lorenzo
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### Author:  [@joshday](https://discourse.julialang.org/u/joshday)
#### Post date: [December 21, 2016, 7:05am UTC](https://discourse.julialang.org/t/proximaloperators-jl/1064/2 "2016-12-21T07:05:50Z")
Thank you, this looks great! Are you familiar with [JuliaML](https://github.com/JuliaML) (ProximalOperators was just mentioned in our [gitter chat](https://gitter.im/JuliaML/chat)), specifically [PenaltyFunctions](https://github.com/JuliaML/PenaltyFunctions.jl)? It looks like we adopted identical syntax for the `prox` function. I’m not sure the form it would take, but there’s a lot of room for working together here.
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### Author:  [@lostella](https://discourse.julialang.org/u/lostella)
#### Post date: [December 21, 2016, 12:43pm UTC](https://discourse.julialang.org/t/proximaloperators-jl/1064/3 "2016-12-21T12:43:34Z")
Hey, no I wasn’t familiar with PenaltyFunctions. Yes, the structure looks similar, although we have used a four-arguments in-place `prox!` method.
**Edit** : another difference is that our implementation of `prox!` returns the function value at the proximal point, along with the proximal point itself.
We currently need to finish implementing [calculus](https://github.com/kul-forbes/ProximalOperators.jl/tree/master/src/calculus) rules: the functions we implemented in ProximalOperators are very customizable with parameters and so on, but an alternative take on a library like this could be to start with elementary functions and build up more complicated ones, using calculus rules to evaluate `prox`. They are not many: conjugation, postcomposition, sum of independent functions are fully working. We need to make the precomposition with an affine mapping more general then what it currently is.
There’s a few more bullets in our [TODO](https://github.com/kul-forbes/ProximalOperators.jl/blob/master/TODO.md) list that need to be addressed. I guess what is also important is to have a scalable method to evaluate `prox` for generic smooth functions.
But yes, there’s a lot we can do together, I agree.
**Edit** : it would also be nice to interface a library like this with some modeling language I suppose.
Thank you!