CVXPY supports Python 3 on Linux, macOS, and Windows. You can use pip or conda for installation. You may want to isolate your installation in a virtualenv, or a conda environment.


(Windows only) Download the Visual Studio build tools for Python 3 (download, install instructions).

(macOS only) Install the Xcode command line tools.

(optional) Create and activate a virtual environment

  1. Install cvxpy.

pip install cvxpy
  1. Test the installation with pytest.

pip install pytest
pytest cvxpy/tests


conda is a system for package and environment management.

(Windows only) Download the Visual Studio build tools for Python 3.

  1. Install conda.

  2. Create a new conda environment,

conda create --name cvxpy_env
conda activate cvxpy_env

or activate an existing one

  1. Install cvxpy from conda-forge

    conda install -c conda-forge cvxpy
  2. Test the installation with pytest.

conda install pytest
pytest cvxpy/tests

Install from source

We strongly recommend using a fresh virtual environment (virtualenv or conda) when installing CVXPY from source.

CVXPY has the following dependencies:

To test the CVXPY installation, you additionally need pytest.

CVXPY automatically installs OSQP, ECOS, SCS. NumPy and SciPy will need to be installed manually, as will Swig . Once you’ve installed these dependencies, perform the following steps:

  1. Clone the official CVXPY git repository, or a newly minted fork of the CVXPY repository.

  2. Navigate to the top-level of the cloned directory.

  3. If you want to use CVXPY with edited source code, run

    pip install -e .

    otherwise, run

    pip install .

Install with CVXOPT and GLPK support

CVXPY supports the CVXOPT solver. Additionally, through CVXOPT, CVXPY supports the GLPK solver. On most platforms, CVXOPT comes with GLPK bundled. On such platforms, installing CVXOPT with

pip install cvxopt

should suffice to get support for both CVXOPT and GLPK.

On other platforms, to install CVXPY and its dependencies with GLPK support, follow these instructions:

  1. Install GLPK. We recommend either installing the latest GLPK from source or using a package manager such as apt-get on Ubuntu and homebrew on OS X.

  2. Install CVXOPT with GLPK bindings.

    pip install cvxopt
  3. Follow the standard installation procedure to install CVXPY and its remaining dependencies.

Install with GUROBI support

CVXPY supports the GUROBI solver. Install GUROBI version 7.5.2 or greater such that you can import gurobipy in Python. See the GUROBI website for installation instructions.

Install with MOSEK support

CVXPY supports the MOSEK solver. Simply install MOSEK such that you can import mosek in Python. See the MOSEK website for installation instructions.

Install with XPRESS support

CVXPY supports the FICO Xpress solver. Simply install XPRESS such that you can import xpress in Python. See the Xpress Python documentation pages for installation instructions.

Install with Cbc (Clp, Cgl) support

CVXPY supports the Cbc solver (which includes Clp and Cgl) with the help of cylp. Simply install cylp (you will need the Cbc sources which includes Cgl) such you can import this library in Python. See the cylp documentation for installation instructions.

Install with CPLEX support

CVXPY supports the CPLEX solver. Simply install CPLEX such that you can import cplex in Python. See the CPLEX website for installation instructions.

Install with SDPT3 support

The sdpt3glue package allows you to model problems with CVXPY and solve them with SDPT3.

Install with NAG support

CVXPY supports the NAG solver. Simply install NAG such that you can import naginterfaces in Python. See the NAG website for installation instructions.

Install with SCIP support

CVXPY supports the SCIP solver. Simply install SCIP such that you can from pyscipopt.scip import Model in Python. See the PySCIPOpt github for installation instructions.

CVXPY’s SCIP interface does not reliably recover dual variables for constraints. If you require dual variables for a continuous problem, you will need to use another solver. We welcome additional contributions to the SCIP interface, to recover dual variables for constraints in continuous problems.

Install with SCIPY support

CVXPY supports the SCIPY solver for LPs. This requires the SciPy package in Python which should already be installed as it is a requirement for CVXPY. SciPy’s “interior-point” and “revised-simplex” implementations are written in python and are always available however the main advantage of this solver, is its ability to use the HiGHS LP solvers (which are written in C++) that comes bundled with SciPy version 1.6.1 and higher.