https://github.com/juliafirstorder/proximalalgorithms.jl

Proximal algorithms for nonsmooth optimization in Julia
https://github.com/juliafirstorder/proximalalgorithms.jl

admm julia machine-learning nonlinear-programming nonsmooth-optimization numerical-optimization optimal-control optimization optimization-algorithms proximal-algorithms proximal-gradient-method proximal-operators

Last synced: 3 months ago
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Proximal algorithms for nonsmooth optimization in Julia

• Host: GitHub
• URL: https://github.com/juliafirstorder/proximalalgorithms.jl
• Owner: JuliaFirstOrder
• License: other
• Created: 2017-10-19T06:32:05.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2025-01-05T16:53:28.000Z (6 months ago)
• Last Synced: 2025-03-29T00:07:09.736Z (3 months ago)
• Topics: admm, julia, machine-learning, nonlinear-programming, nonsmooth-optimization, numerical-optimization, optimal-control, optimization, optimization-algorithms, proximal-algorithms, proximal-gradient-method, proximal-operators
• Language: Julia
• Homepage:
• Size: 5.79 MB
• Stars: 133
• Watchers: 10
• Forks: 22
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# ProximalAlgorithms.jl

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[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliafirstorder.github.io/ProximalAlgorithms.jl/stable)

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A Julia package for non-smooth optimization algorithms.

This package provides algorithms for the minimization of objective functions

that include non-smooth terms, such as constraints or non-differentiable penalties.

Implemented algorithms include:

- (Fast) Proximal gradient methods

- Douglas-Rachford splitting

- Three-term splitting

- Primal-dual splitting algorithms

- Newton-type methods

Check out [this section](https://juliafirstorder.github.io/ProximalAlgorithms.jl/stable/guide/implemented_algorithms/) for an overview of the available algorithms.

Algorithms rely on:

- [DifferentiationInterface.jl](https://github.com/gdalle/DifferentiationInterface.jl) for automatic differentiation (but you can easily bring your own gradients)

- the [ProximalCore API](https://github.com/JuliaFirstOrder/ProximalCore.jl) for proximal mappings, projections, etc, to handle non-differentiable terms (see for example [ProximalOperators](https://github.com/JuliaFirstOrder/ProximalOperators.jl) for an extensive collection of functions).

## Documentation

[Stable version](https://juliafirstorder.github.io/ProximalAlgorithms.jl/stable) (latest release)

[Development version](https://juliafirstorder.github.io/ProximalAlgorithms.jl/dev) (`master` branch)

## Citing

If you use any of the algorithms from ProximalAlgorithms in your research, you are kindly asked to cite the relevant bibliography.

Please check [this section of the manual](https://juliafirstorder.github.io/ProximalAlgorithms.jl/stable/guide/implemented_algorithms/) for algorithm-specific references.

## Contributing

Contributions are welcome in the form of [issues notification](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl/issues) or [pull requests](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl/pulls). We recommend looking at already implemented algorithms to get inspiration on how to structure new ones.