Install

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.

pip

(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.

Install CVXPY using pip:

pip install cvxpy

You can add solver names as “extras”; pip will then install the necessary additional Python packages.

pip install cvxpy[CBC,CVXOPT,GLOP,GLPK,GUROBI,MOSEK,PDLP,SCIP,XPRESS]

conda

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

Install from source

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

CVXPY has the following dependencies:

All required packages are installed automatically alongside CVXPY.

Perform the following steps to install CVXPY from source:

  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 editable source code, run

    pip install -e .

    otherwise, run

    pip install .

Using Codespaces

We provide support for GitHub Codespaces with preconfigured environments for CVXPY development via devcontainers. To get started, click the “Code” button on the CVXPY repository and select “Open with Codespaces”.

Apple M1 users

Apple M1 users have had trouble installing CVXPY using the commands above. That trouble stemmed partly from a configuration error in CVXPY’s pyproject.toml, which has been fixed in CVXPY 1.1.19 and 1.2.0. If you have those versions (or newer) then the above commands should work provided (1) you have cmake installed via Homebrew and (2) you have an ECOS 2.0.5 wheel. The cmake requirement stems from OSQP and there appear to be problems building more recent versions of ECOS on M1 machines. See this comment on the CVXPY repo and this issue on the ECOS repo for more information.

Running the test suite

CVXPY comes with an extensive test suite, which can be run after installing pytest. If installed from source, navigate to the root of the repository and run

pytest

To run the tests when CVXPY was not installed from source, use

pytest --pyargs cvxpy.tests

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.

    CVXOPT_BUILD_GLPK=1
CVXOPT_GLPK_LIB_DIR=/path/to/glpk-X.X/lib
CVXOPT_GLPK_INC_DIR=/path/to/glpk-X.X/include
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 and the corresponding prerequisites according to the instructions, such you can import this library in Python.

Install with COPT support

CVXPY supports the COPT solver. Simply install COPT such that you can import coptpy in Python. See the COPT release page 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 SDPA support

CVXPY supports the SDPA solver. Simply install SDPA for Python such that you can import sdpap in Python. See the SDPA for Python 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 GLOP and PDLP support

CVXPY supports the GLOP and PDLP solvers. Both solvers are provided by the open source OR-Tools package. Install OR-Tools such that you can run import ortools in Python. OR-Tools version 9.3 or greater is required.

Install with SCIP support

CVXPY supports the SCIP solver through the pyscipopt Python package. 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 and MIPs. 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 and MIP solvers (which are written in C++). HiGHS LP solvers come bundled with SciPy version 1.6.1 and higher. The HiGHS MIP solver comes bundled with version 1.9.0 and higher.

Install with CLARABEL support

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

Install with PIQP support

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

Install with PROXQP support

CVXPY supports the PROXQP solver. Simply install PROXQP such that you can import proxsuite in Python. See the proxsuite github for installation instructions. Be aware that PROXQP by default uses dense matrices to represent problem data. You may achieve better performance by setting backend = 'sparse' in your call to problem.solve.

Install without default solvers

CVXPY can also be installed without the default solver dependencies. This can be useful if the intention is to only use non-default solvers.

The solver-less installation, cvxpy-base, can currently be installed through pip and conda.

Installing using pip:

pip install cvxpy-base

Installing using conda:

conda install cvxpy-base