---
id: cvxopt
version: "1.3.3"
license: CVXOPT version 1.3 Copyright (c) 2012-2026 M. Andersen and L. Vandenberghe. Copyright (c) 2010-2011 L. Vandenberghe. Copyright (c) 2004-2009 J. Dahl and L. Vandenberghe. This program is free… (full text in the JSON record)
license_treatment: unclear
maintenance: active
---

# cvxopt — Convex optimization package

License: unclear · Maintenance: active · Downloads: 1.3M/mo

## What it is and what it does

cvxopt is a mature Python package for solving convex optimization problems across linear, quadratic, and semidefinite programming domains. It provides low-level access to optimization algorithms and matrix data structures, making it suitable for researchers and practitioners who need fine-grained control over solver behavior and problem formulation.

The package is built on compiled C code for numerical performance and depends on standard linear algebra libraries (BLAS, LAPACK). It has no Python runtime dependencies, keeping the footprint minimal. The project is actively maintained, with recent commits and a stable API, though its GPLv3 license restricts use in proprietary closed-source applications.

Use it for:

- Solve linear and quadratic programming problems in operations research or portfolio optimization workflows.
- Formulate and solve semidefinite programs for control theory, signal processing, or machine learning applications.
- Prototype convex optimization algorithms in research where direct access to solver internals is needed.
- Build optimization-based decision systems in academic or open-source projects where GPLv3 licensing is acceptable.

## Worth the install?

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

cvxopt is a Python library for solving convex optimization problems, providing algorithms and data structures for linear, quadratic, and semidefinite programming.

Yes, if you need convex optimization and accept GPLv3 licensing. The package is production-stable, actively maintained, and has no Python dependencies. Install friction is moderate due to compiled components, but prebuilt wheels cover common platforms. Not suitable for proprietary closed-source projects due to GPLv3 copyleft requirements.

## Install

pip install cvxopt
uv add cvxopt
poetry add cvxopt

## Installing cvxopt

Before you install: Medium install friction due to compiled C components; wheels are available for Python 3.10–3.12 on macOS, Linux, and Windows, but source builds may require a C compiler and numerical libraries.

License in practice: Licensed under GNU General Public License v3, which requires that any derivative work or distribution must also be released under GPLv3 and provide source code access—a significant constraint for proprietary or closed-source projects.

Quickstart:

pip install cvxopt

from cvxopt import matrix, solvers
A = matrix([[1.0, -1.0], [1.0, 1.0]])
b = matrix([1.0, 2.0])
sol = solvers.qp(P, q, G, h)

Requires a C compiler and numerical libraries (BLAS, LAPACK) for source builds; prebuilt wheels simplify installation on common platforms.

Verify before relying:

- Whether cvxopt's solver performance and numerical stability are suitable for your specific problem scale and precision requirements.
- Compatibility with other optimization frameworks or whether cvxopt's API and algorithm selection meet your workflow needs.

## Package facts

- License: CVXOPT version 1.3 Copyright (c) 2012-2026 M. Andersen and L. Vandenberghe. Copyright (c) 2010-2011 L. Vandenberghe. Copyright (c) 2004-2009 J. Dahl and L. Vandenberghe. This program is free… (full text in the JSON record) (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 1.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

convex optimization solver, linear programming python, semidefinite programming, quadratic programming library, optimization algorithms, conic optimization, mathematical optimization, optimization, convex-programming, numerical-computing

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