Getting Started
===============

This guide will provide you with the necessary steps to get started with Linopy, from installation to creating your first model and beyond.

Before you start, make sure you have the following:

- Python 3.9 or later installed on your system.
- Basic knowledge of Python and linear programming.


Install Linopy
--------------

You can install Linopy using pip or conda. Here are the commands for each method:

.. code-block:: bash

   pip install linopy

or

.. code-block:: bash

   conda install -c conda-forge linopy


Install a solver
----------------

Linopy won't work without a solver. Currently, the following solvers are supported:

CPU-based solvers
~~~~~~~~~~~~~~~~~

-  `Cbc <https://projects.coin-or.org/Cbc>`__ - open source, free, fast
-  `GLPK <https://www.gnu.org/software/glpk/>`__ - open source, free, not very fast
-  `HiGHS <https://highs.dev/>`__ - open source, free, fast
-  `Gurobi <https://www.gurobi.com/>`__  - closed source, commercial, very fast
-  `Xpress <https://www.fico.com/en/fico-xpress-trial-and-licensing-options>`__ - closed source, commercial, very fast (GPU acceleration available in v9.8+)
-  `Cplex <https://www.ibm.com/de-de/analytics/cplex-optimizer>`__ - closed source, commercial, very fast
-  `MOSEK <https://www.mosek.com/>`__
-  `MindOpt <https://solver.damo.alibaba.com/doc/en/html/index.html>`__ -
-  `COPT <https://www.shanshu.ai/copt>`__ - closed source, commercial, very fast

For a subset of the solvers, Linopy provides a wrapper.

.. code:: bash

    pip install linopy[solvers]


We recommend to install the HiGHS solver if possible, which is free and open source but not yet available on all platforms.

.. code:: bash

    pip install highspy


GPU-accelerated solvers
~~~~~~~~~~~~~~~~~~~~~~~

For large-scale optimization problems, GPU-accelerated solvers can provide significant performance improvements:

-  `cuPDLPx <https://github.com/MIT-Lu-Lab/cuPDLPx>`__ - open source, GPU-accelerated first-order solver

**Note:** GPU solvers require compatible NVIDIA GPU hardware and CUDA installation. See the :doc:`gpu-acceleration` guide for detailed setup instructions.

.. code:: bash

    pip install cupdlpx


For most of the other solvers, please click on the links to get further installation information.



If you're ready, let's dive in!