---
id: cvxpy-base
version: "1.9.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# cvxpy-base — A domain-specific language for modeling convex optimization problems in Python.

License: permissive · Maintenance: active · Downloads: 222.0K/mo

## What it is and what it does

cvxpy-base is the compiled solver kernel layer for CVXPY, a Python-embedded domain-specific language for convex optimization. It provides the numerical computation engine that solves optimization problems modeled in CVXPY's high-level syntax. The package handles the lower-level solver operations, relying on numpy, scipy, qdldl, and sparsediffpy for linear algebra and differentiation.

This is a base package—part of CVXPY's architecture rather than a standalone tool. It enables CVXPY to model and solve convex optimization problems (including mixed-integer, geometric, quasiconvex, and nonlinear programs) by providing the compiled solver kernels that perform the actual numerical work. Users typically interact with it indirectly through the main cvxpy package, which uses cvxpy-base's solvers to compute solutions.

Use it for:

- Solving least-squares problems with variable bounds or linear constraints in machine learning pipelines
- Portfolio optimization and risk management in quantitative finance applications
- Control system design and trajectory optimization in robotics and autonomous systems
- Signal processing and filter design requiring convex formulations
- Resource allocation and scheduling problems in operations research

## Worth the install?

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

cvxpy-base provides compiled solver kernels for CVXPY, a Python modeling language for convex optimization problems. It handles the numerical computation layer that solves optimization problems expressed in CVXPY's high-level syntax.

Yes, if you are using CVXPY for convex optimization work. cvxpy-base is a required component of CVXPY's solver stack. It is actively maintained, has no known vulnerabilities, supports current Python versions, and carries a permissive Apache-2.0 license. Install friction is moderate due to compiled wheels, but pre-built binaries for major platforms minimize build complexity.

## Install

pip install cvxpy-base
uv add cvxpy-base
poetry add cvxpy-base

## Installing cvxpy-base

Before you install: Medium install friction due to compiled wheels for multiple platforms and Python versions. Active maintenance with recent releases; last commit 2026-08-12 indicates steady support.

License in practice: Apache-2.0 permissive license allows use in commercial and open-source projects with minimal restrictions; attribution required but no copyleft obligations.

Quickstart:

pip install cvxpy-base

import cvxpy as cp
import numpy

x = cp.Variable(10)
objective = cp.Minimize(cp.sum_squares(x))
prob = cp.Problem(objective)
prob.solve()

Requires Python >= 3.11. Runtime dependencies numpy, scipy, qdldl, and sparsediffpy must be installed; typically handled automatically by pip.

Verify before relying:

- Whether cvxpy-base can be installed and used standalone or requires the main cvxpy package
- Performance characteristics and solver selection behavior compared to other CVXPY solver backends
- Specific optimization problem classes or sizes for which this backend is recommended
- How monthly download volume translates to actual production usage patterns

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 222.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

convex optimization solver, cvxpy backend, optimization problem solver, mathematical optimization, quadratic programming, semidefinite programming, convex problem solving, optimization, convex-programming, solver-backend

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