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 | 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
Clarabel Conic Interior Point Solver for Rust / Python
We found that clarabel demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago.It has 1 open source maintainer collaborating on the project.
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