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https://github.com/oxfordcontrol/clarabel.rs
Clarabel.rs: Interior-point solver for convex conic optimisation problems in Rust.
https://github.com/oxfordcontrol/clarabel.rs
conic-optimization conic-programs convex-optimization interior-point-method linear-programming optimization optimization-algorithms quadratic-programming rust-lang semidefinite-programming
Last synced: 2 days ago
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Clarabel.rs: Interior-point solver for convex conic optimisation problems in Rust.
- Host: GitHub
- URL: https://github.com/oxfordcontrol/clarabel.rs
- Owner: oxfordcontrol
- License: apache-2.0
- Created: 2022-04-17T13:48:34.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-14T17:12:49.000Z (9 months ago)
- Last Synced: 2024-03-15T18:14:36.641Z (9 months ago)
- Topics: conic-optimization, conic-programs, convex-optimization, interior-point-method, linear-programming, optimization, optimization-algorithms, quadratic-programming, rust-lang, semidefinite-programming
- Language: Rust
- Homepage:
- Size: 995 KB
- Stars: 237
- Watchers: 9
- Forks: 17
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.md
Awesome Lists containing this project
README
Interior Point Conic Optimization for Rust and Python
Features •
Installation •
License •
Documentation
__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:
$$
\begin{array}{r}
\text{minimize} & \frac{1}{2}x^T P x + q^T x\\\\[2ex]
\text{subject to} & Ax + s = b \\\\[1ex]
& s \in \mathcal{K}
\end{array}
$$
with decision variables
$x \in \mathbb{R}^n$,
$s \in \mathbb{R}^m$
and data matrices
$P=P^\top \succeq 0$,
$q \in \mathbb{R}^n$,
$A \in \mathbb{R}^{m \times n}$, and
$b \in \mathbb{R}^m$.
The convex set $\mathcal{K}$ is a composition of convex cones.
__For more information see the Clarabel Documentation ([stable](https://clarabel.org) | [dev](https://clarabel.org/dev)).__
Clarabel is also available in a Julia implementation. See [here](https://github.com/oxfordcontrol/Clarabel.jl).
## 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](https://github.com/oxfordcontrol/Clarabel.rs) and distributed under the Apache 2.0 License
# Installation
Clarabel can be imported to Cargo based Rust projects by adding
```rust
[dependencies]
clarabel = "0"
```
to the project's `Cargo.toml` file. To install from source, see the [Rust Installation Documentation](https://oxfordcontrol.github.io/ClarabelDocs/stable/rust/installation_rs/).
To use the Python interface to the solver:
```
pip install clarabel
```
To install the Python interface from source, see the [Python Installation Documentation](https://oxfordcontrol.github.io/ClarabelDocs/stable/python/installation_py/).
## 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](LICENSE.md) file for details.