clarabel 0.1.0

Clarabel Conic Interior Point Solver for Rust / Python
Documentation 
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<h1 align="center" margin=0px>
  <img src="https://github.com/oxfordcontrol/ClarabelDocs/blob/main/docs/src/assets/logo-banner-light-rs.png#gh-light-mode-only" width=60%>
  <img src="https://github.com/oxfordcontrol/ClarabelDocs/blob/main/docs/src/assets/logo-banner-dark-rs.png#gh-dark-mode-only"   width=60%>
  <br>
Interior Point Conic Optimization for Rust and Python
</h1>
<p align="center">
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</p>

<p align="center">
  <a href="#features">Features</a> •
  <a href="#installation">Installation</a> •
  <a href="#license-">License</a> •
  <a href="https://oxfordcontrol.github.io/ClarabelDocs/stable">Documentation</a>
</p>

__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://oxfordcontrol.github.io/ClarabelDocs/stable) |  [dev](https://oxfordcontrol.github.io/ClarabelDocs/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) and second-order cone programs (SOCPs).  Future versions will provide support for problems involving positive semidefinite, exponential and power cones.
* __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/rust/installation_py/).

## License 🔍
This project is licensed under the Apache License - see the [LICENSE.md](LICENSE.md) file for details.