---
id: clarabel
version: "0.11.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# clarabel — Clarabel Conic Interior Point Solver for Rust / Python

License: permissive · Maintenance: active · Downloads: 4.0M/mo

## What it is and what it does

Clarabel is a Rust-based interior-point solver for convex optimization problems, exposed as a Python package. It solves conic programs—including linear programs, quadratic programs, second-order cone programs, and semidefinite programs—by reformulating them into a standard conic form and applying a homogeneous embedding technique. Unlike many interior-point solvers, it handles quadratic objectives directly without requiring reformulation, which can improve performance for problems with quadratic cost functions.

The package depends on numpy, scipy, and cffi for numerical operations and Python-Rust interoperability. It is actively maintained, supports current Python versions (3.9+), and provides prebuilt wheels for common architectures, reducing installation friction. The solver detects infeasible problems and supports a range of cone types including exponential and power cones, making it suitable for diverse optimization tasks in machine learning, control, finance, and engineering.

Use it for:

- Solve portfolio optimization and risk management problems with quadratic objectives and linear constraints
- Formulate and solve machine learning problems like support vector machines and robust regression as conic programs
- Detect infeasibility in constraint systems and provide certificates of infeasibility
- Solve control and robotics problems involving second-order cone constraints
- Benchmark and compare interior-point methods on semidefinite programming instances

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Clarabel is an interior-point conic optimization solver that handles linear programs, quadratic programs, second-order cone programs, semidefinite programs, and problems with exponential and power cone constraints.

Yes, if you need a robust, actively maintained conic optimizer with native Python bindings. The permissive Apache-2.0 license, zero known vulnerabilities, and support for modern Python versions make it a solid choice for convex optimization. Install friction is moderate due to compiled bindings, but prebuilt wheels cover common platforms. Not necessary if you already use a different solver or if your problems are outside the conic optimization scope.

## Install

pip install clarabel
uv add clarabel
poetry add clarabel

## Installing clarabel

Before you install: Medium install friction due to compiled Rust bindings; prebuilt wheels available for common platforms (x86_64, ARM64 on Linux/macOS/Windows). Requires Python 3.9+. Active maintenance with recent commits and steady release cadence.

License in practice: Apache-2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions; suitable for proprietary projects.

Quickstart:

pip install clarabel

import clarabel
import numpy as np

# Define a simple quadratic program
P = np.eye(2)
q = np.array([1.0, 2.0])
A = np.array([[1.0, 1.0]])
b = np.array([1.0])

solver = clarabel.DefaultSolver(P, q, A, b)
result = solver.solve()

Requires Python 3.9 or later. Compiled Rust extension; if prebuilt wheels unavailable for your platform, installation requires a Rust toolchain.

Verify before relying:

- Numerical stability and convergence guarantees for specific problem classes or scales
- Performance comparison with other interior-point solvers on standard benchmarks
- Memory requirements for large-scale semidefinite programs

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 4.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

conic optimization solver, convex optimization python, interior point solver, semidefinite programming, quadratic programming solver, second-order cone programming, linear programming solver, optimization, convex-programming, numerical-solver

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