Proximal operators for nonsmooth optimization in Julia
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
demos
docs added IndHyperslab, improved IndHalfspace Nov 17, 2018
logo
src
test
.gitignore new logo (#48) Mar 7, 2018
.travis.yml Julia 1.0 update (#59) Sep 4, 2018
LICENSE.md changed package name Oct 26, 2016
README.md
REQUIRE Julia 1.0 update (#59) Sep 4, 2018
appveyor.yml

README.md

ProximalOperators.jl

Build Status Build status codecov Gitter

Proximal operators for nonsmooth optimization in Julia. This package can be used to easily implement proximal algorithms for convex and nonconvex optimization problems such as ADMM, the alternating direction method of multipliers.

See the documentation on how to use the package.

Installation

To install the package, hit ] from the Julia command line to enter the package manager, then

pkg> add ProximalOperators

Usage

With using ProximalOperators the package exports the prox and prox! methods to evaluate the proximal mapping of several functions.

A list of available function constructors is in the documentation.

For example, you can create the L1-norm as follows.

julia> f = NormL1(3.5)
description : weighted L1 norm
type        : Array{Complex} → Real
expression  : x ↦ λ||x||_1
parameters  : λ = 3.5

Functions created this way are, of course, callable.

julia> x = randn(10) # some random point
julia> f(x)
32.40700818735099

prox evaluates the proximal operator associated with a function, given a point and (optionally) a positive stepsize parameter, returning the proximal point y and the value of the function at y:

julia> y, fy = prox(f, x, 0.5) # last argument is 1.0 if absent

prox! evaluates the proximal operator in place, and only returns the function value at the proximal point:

julia> fy = prox!(y, f, x, 0.5) # in-place equivalent to y, fy = prox(f, x, 0.5)

Related packages

References

  1. N. Parikh and S. Boyd (2014), Proximal Algorithms, Foundations and Trends in Optimization, vol. 1, no. 3, pp. 127-239.

  2. S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein (2011), Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1-122.

Credits

ProximalOperators.jl is developed by Lorenzo Stella and Niccolò Antonello at KU Leuven, ESAT/Stadius, and Mattias Fält at Lunds Universitet, Department of Automatic Control.