============

Ceres Solver

============

Ceres Solver [#f1]_ is an open source C++ library for modeling and

solving large, complicated optimization problems. It can be used to

solve `Non-linear Least Squares`_ problems with bounds constraints and

general unconstrained optimization problems. It is a mature, feature

rich, and performant library that has been used in production at

Google since 2010. For more, see :doc:`features`.

`ceres-solver@googlegroups.com

<https://groups.google.com/forum/?fromgroups#!forum/ceres-solver>`_ is

the place for discussions and questions about Ceres Solver. We use the

`GitHub Issue Tracker

<https://github.com/ceres-solver/ceres-solver/issues>`_ to manage bug

reports and feature requests.

.. toctree::

   :maxdepth: 1

   :hidden:

   features

   installation

   tutorial

   derivatives

   nnls_modeling

   nnls_solving

   nnls_covariance

   gradient_solver

   faqs

   users

   contributing

   version_history

   bibliography

   license

.. _Non-linear Least Squares: http://en.wikipedia.org/wiki/Non-linear_least_squares

Cite Us

=======

If you use Ceres Solver for a publication, please cite it as::

    @misc{ceres-solver,

      author = "Sameer Agarwal and Keir Mierle and Others",

      title = "Ceres Solver",

      howpublished = "\url{http://ceres-solver.org}",

    }

.. rubric:: Footnotes

.. [#f1] While there is some debate as to who invented the method of

         Least Squares [Stigler]_, there is no questioning the fact

         that it was `Carl Friedrich Gauss

         <http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Gauss.html>`_

         who brought it to the attention of the world. Using just 22

         observations of the newly discovered asteroid `Ceres

         <http://en.wikipedia.org/wiki/Ceres_(dwarf_planet)>`_, Gauss

         used the method of least squares to correctly predict when

         and where the asteroid will emerge from behind the Sun

         [TenenbaumDirector]_. We named our solver after Ceres to

         celebrate this seminal event in the history of astronomy,

         statistics and optimization.