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

Features

  • Documentation available
  • Examples available
  • Proximal operators for nonsmooth optimization in Julia
  • This package can be used to easily implement proximal algorithms for convex and nonconvex optimization problems
  • ADMM, the alternating direction method of multipliers
  • Licensed under the MIT License

Project Samples

ProximalOperators.jl Screenshot 1

Project Activity

See All Activity >

Categories

Data Visualization

License

MIT License

Follow ProximalOperators.jl

ProximalOperators.jl Web Site

Other Useful Business Software
Full-stack observability with actually useful AI | Grafana Cloud Icon
Full-stack observability with actually useful AI | Grafana Cloud

Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

Built on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
Create free account
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of ProximalOperators.jl!

Additional Project Details

Programming Language

Julia

Related Categories

Julia Data Visualization Software

Registered

2023-11-30
Report inappropriate content