文件列表:

.cargo (0, 2023-06-03)
.cargo\config.toml (237, 2023-06-03)
CHANGELOG.md (3728, 2023-06-03)
Cargo.toml (3748, 2023-06-03)
LICENSE.md (11357, 2023-06-03)
examples (0, 2023-06-03)
examples\python (0, 2023-06-03)
examples\python\example_expcone.py (613, 2023-06-03)
examples\python\example_powcone.py (689, 2023-06-03)
examples\python\example_qp.py (950, 2023-06-03)
examples\python\example_sdp.py (710, 2023-06-03)
examples\python\example_socp.py (650, 2023-06-03)
examples\rust (0, 2023-06-03)
examples\rust\example_box.rs (807, 2023-06-03)
examples\rust\example_expcone.rs (711, 2023-06-03)
examples\rust\example_lp.rs (985, 2023-06-03)
examples\rust\example_powcone.rs (1141, 2023-06-03)
examples\rust\example_qp.rs (1756, 2023-06-03)
examples\rust\example_sdp.rs (830, 2023-06-03)
examples\rust\example_socp.rs (598, 2023-06-03)
html (0, 2023-06-03)
html\rustdocs-header.html (696, 2023-06-03)
pyproject.toml (270, 2023-06-03)
python (0, 2023-06-03)
python\clarabel (0, 2023-06-03)
python\clarabel\__init__.py (152, 2023-06-03)
rustfmt.toml (38, 2023-06-03)
src (0, 2023-06-03)
src\algebra (0, 2023-06-03)
src\algebra\adjoint.rs (348, 2023-06-03)
src\algebra\csc (0, 2023-06-03)
src\algebra\csc\block_concatenate.rs (1998, 2023-06-03)
src\algebra\csc\core.rs (12501, 2023-06-03)
... ...

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://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), second-order cone programs (SOCPs) and semidefinite programs (SDPs). It also solves problems with exponential and 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/). ## License ” This project is licensed under the Apache License 2.0 - see the [LICENSE.md](LICENSE.md) file for details.

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