.. tab:: pip

    (Windows only) `Download <https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16>`_ the Visual Studio build tools for Python 3
    (`instructions <https://docs.google.com/presentation/d/e/2PACX-1vT-p04simYhorAdKstO9F1RK-k6npuyrKWliJ8Wy9uuQoQq_TiFdJA-DK3Kz0irkCEUlmNEH4JScbkwUflXv9c/pub?start=false&loop=false&delayms=3000&resourcekey=0-HEezB2NFstz1GjKDkroJSQ&slide=id.p1>`_).

    (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,KNITRO]"

.. tab:: conda

    `conda`_ is a system for package and environment management.

    (Windows only) Download the `Visual Studio build tools for Python 3 <https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16>`_.

    1. Install `conda`_.

    2. Create a new conda environment,
    ::

        conda create --name cvxpy_env
        conda activate cvxpy_env

    or activate an existing one

    3. Install ``cvxpy`` from `conda-forge <https://conda-forge.org/>`_
    ::

        conda install -c conda-forge cvxpy

.. tab:: Install from source

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

    CVXPY has the following dependencies:

    * Python >= 3.11
    * `OSQP`_ >= 0.6.2
    * `CLARABEL`_ >= 0.6.0
    * `SCS`_ >= 3.2.4.post1
    * `NumPy`_ >= 1.22.4
    * `SciPy`_ >= 1.13.0

    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 .

.. tab:: Using Codespaces

    We provide support for `GitHub Codespaces <https://github.com/features/codespaces>`_ with
    preconfigured environments for CVXPY development via `devcontainers <https://containers.dev/>`_.
    To get started, click the "Code" button on the CVXPY repository and select "Open with Codespaces".

CVXPY supports the `CVXOPT`_ solver.
Additionally, through CVXOPT, CVXPY supports the `GLPK`_ solver. On `most
platforms <https://cvxopt.org/install/index.html#installing-a-pre-built-package>`_,
`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 <https://www.gnu.org/software/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.

CVXPY supports the GUROBI solver.
Install GUROBI version 7.5.2 or greater such that you can ``import gurobipy`` in Python.
See the `GUROBI <https://www.gurobi.com/>`_ website for installation instructions.

CVXPY supports the Moreau solver, a GPU-accelerated conic optimization solver.
Contact `Optimal Intellect <https://www.optimalintellect.com/>`_ for installation
instructions and system requirements.

CVXPY supports the MOSEK solver.
Simply install MOSEK such that you can ``import mosek`` in Python.
See the `MOSEK <https://www.mosek.com/>`_ website for installation instructions.

CVXPY supports the MPAX solver.
Simply install MPAX such that you can ``import mpax`` in Python.
See the `MPAX <https://github.com/MIT-Lu-Lab/MPAX>`_ website for installation instructions.
In addition, please refer to the `JAX installation instructions <https://docs.jax.dev/en/latest/installation.html>`_ to install JAX.

CVXPY supports the FICO Xpress solver.
Simply install XPRESS such that you can ``import xpress`` in Python.
See the `Xpress Python documentation <https://www.fico.com/fico-xpress-optimization/docs/latest/solver/optimizer/python/HTML/GUID-616C323F-05D8-3460-B0D7-80F77DA7D046.html>`_ pages for installation instructions.

CVXPY supports the `Cbc <https://github.com/coin-or/Cbc>`_ solver (which includes Clp and Cgl) with the help of `cylp <https://github.com/coin-or/CyLP>`_.
Simply install cylp and the corresponding prerequisites according to the `instructions <https://github.com/coin-or/CyLP#cylp>`_, such you can import this library in Python.

CVXPY supports the COPT solver.
Simply install COPT such that you can ``import coptpy`` in Python.
See the `COPT <https://github.com/COPT-Public/COPT-Release>`_ release page for installation instructions.

CVXPY supports the CPLEX solver.
Simply install CPLEX such that you can ``import cplex`` in Python.
See the `CPLEX <https://www.ibm.com/support/knowledgecenter/SSSA5P>`_ website for installation instructions.

CVXPY supports the SDPA solver.
Simply install SDPA for Python such that you can ``import sdpap`` in Python.
See the `SDPA for Python <https://sdpa-python.github.io/docs/installation>`_ website for installation instructions.

The `sdpt3glue package <https://github.com/TrishGillett/pysdpt3glue>`_ allows you to model problems with CVXPY and solve them with SDPT3.

CVXPY supports the NAG solver.
Simply install NAG such that you can ``import naginterfaces`` in Python.
See the `NAG <https://support.nag.com/numeric/py/nagdoc_latest/readme.html>`_ website for installation instructions.

CVXPY supports the GLOP and PDLP solvers. Both solvers are provided by
the open source `OR-Tools <https://github.com/google/or-tools>`_ package.
Install OR-Tools such that you can run ``import ortools`` in Python. OR-Tools
version 9.3 or greater is required.

CVXPY supports the SCIP solver through the ``pyscipopt`` Python package.
See the `PySCIPOpt <https://github.com/SCIP-Interfaces/PySCIPOpt#installation>`_ 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.

   pip install highspy

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.

CVXPY supports the PIQP solver.
Simply install PIQP such that you can ``import piqp`` in Python.
See the `PIQP <https://predict-epfl.github.io/piqp/interfaces/python/installation>`_ website for installation instructions.

CVXPY supports the PROXQP solver.
Simply install PROXQP such that you can ``import proxsuite`` in Python.
See the `proxsuite <https://github.com/simple-robotics/proxsuite#quick-install>`_ 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``.

CVXPY supports the QPALM solver.
Simply install QPALM such that you can ``import qpalm`` in Python.
See the `QPALM <https://kul-optec.github.io/QPALM/Doxygen/index.html>`_ website for installation instructions.

CVXPY supports the QOCO solver.
Simply install QOCO such that you can ``import qoco`` in Python.
See the `QOCO <https://qoco-org.github.io/qoco/qoco/install/python.html#python-installation>`_ website for installation instructions.

CVXPY supports the CUOPT solver.
Simply install CUOPT such that you can ``import cuopt`` in Python.
See the `CUOPT github page <http://github.com/NVIDIA/cuopt>`_ for system requirments and installation instructions (pip, conda, or build from source).
Follow this link for additional documentation on CUOPT_

CVXPY supports the KNITRO solver.
Simply install KNITRO such that you can ``import knitro`` in Python. You can do this by running

.. code-block:: python

   pip install knitro

See the `KNITRO <https://www.artelys.com/solvers/knitro/>`_ website for additional instructions.

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:

.. code-block:: python

    pip install cvxpy-base

Installing using conda:

.. code-block:: python

    conda install cvxpy-base

pytest

pytest --pyargs cvxpy.tests