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Clarabel.rs: Interior-point solver for convex conic optimisation problems in Rust.

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Clarabel.jl logo

Interior Point Conic Optimization for Rust and Python

FeaturesInstallationLicenseDocumentation

Clarabel.rs is a Rust implementation of an interior point numerical solver for convex optimization problems using a novel homogeneous embedding. Clarabel.rs solves the following problem:

minimize 1 2 x T P x + q T x subject to A x + s = b s K

with decision variables x R n , s R m and data matrices P = P 0 , q R n , A R m × n , and b R m . The convex set K is a composition of convex cones.

For more information see the Clarabel Documentation (stable | dev).

Clarabel is also available in a Julia implementation. See here.

Features

  • Versatile: Clarabel.rs solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs) and semidefinite programs (SDPs). It also solves problems with exponential, power cone and generalized power cone constraints.
  • Quadratic objectives: Unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE), Clarabel.rs handles quadratic objectives without requiring any epigraphical reformulation of the objective. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions.
  • Infeasibility detection: Infeasible problems are detected using a homogeneous embedding technique.
  • Open Source: Our code is available on GitHub and distributed under the Apache 2.0 License

Installation

Clarabel can be imported to Cargo based Rust projects by adding

[dependencies]
clarabel = "0"  

to the project's Cargo.toml file. To install from source, see the Rust Installation Documentation.

To use the Python interface to the solver:

pip install clarabel

To install the Python interface from source, see the Python Installation Documentation.

Citing

@misc{Clarabel_2024,
      title={Clarabel: An interior-point solver for conic programs with quadratic objectives}, 
      author={Paul J. Goulart and Yuwen Chen},
      year={2024},
      eprint={2405.12762},
      archivePrefix={arXiv},
      primaryClass={math.OC}
}

License 🔍

This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details.