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:
$$ \begin{array}{r} \text{minimize} & \frac{1}{2}xT P x + qT x\\[2ex] \text{subject to} & Ax + s = b \\[1ex] & s \in \mathcal{K} \end{array} $$
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 | dev).
Clarabel is also available in a Julia implementation. See here.
Features
- Versatile: Clarabel.rs solves linear programs (LPs), quadratic programs (QPs) and second-order cone programs (SOCPs). It also solves problems with exponential and power cone constraints. Future versions will provide support for problems involving positive semidefinite 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
= "0"
clarabel
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.
License 🔍
This project is licensed under the Apache License - see the LICENSE.md file for details.