docs/introduction.tex - platform/external/ceres-solver - Git at Google

 %!TEX root = ceres-solver.tex
 \chapter{Introduction}
 \label{chapter:introduction}
 Ceres Solver\footnote{For brevity, in the rest of this document we will just use the term Ceres.} is a non-linear least squares solver developed at Google. It is designed to solve small and large sparse problems accurately and efficiently~\footnote{For a gentle but brief introduction to non-liner least squares problems, please start by reading the~\hyperref[part:tutorial]{Tutorial}}. Amongst its various features is a simple but expressive API with support for automatic differentiation, robust norms, local parameterizations, automatic gradient checking, multithreading and automatic problem structure detection.

 The key computational cost when solving a non-linear least squares problem is the solution of a linear least squares problem in each iteration. To this end Ceres supports a number of different linear solvers suited for different needs. This includes dense QR factorization (using \eigen) for small scale problems, sparse Cholesky factorization (using \texttt{SuiteSparse}) for general sparse problems and specialized Schur complement based solvers for problems that arise in multi-view geometry~\cite{hartley-zisserman-book-2004}.

 Ceres has been used for solving a variety of problems in computer vision and machine learning at Google with sizes that range from a tens of variables and objective functions with a few hundred terms to problems with millions of variables and objective functions with tens of millions of terms.


 \section{What's in a name?}
 While there is some debate as to who invented of the method of Least Squares~\cite{stigler1981gauss}. There is no debate that it was Carl Friedrich Gauss's prediction of the orbit of the newly discovered asteroid Ceres based on just 41 days of observations that brought it to the attention of the world~\cite{tennenbaum-director}. We named our solver after Ceres to celebrate this seminal event in the history of astronomy, statistics and optimization.

 \section{Contributing to Ceres Solver}

 We welcome contributions to Ceres, whether they are new features, bug fixes or tests. The Ceres mailing list\footnote{\url{http://groups.google.com/group/ceres-solver}} is the best place for all development related discussions. Please consider joining it. If you have ideas on how you would like to contribute to Ceres, it is a good idea to let us know on the mailinglist before you start development. We may have suggestions that will save effort when trying to merge your work into the main branch. If you are looking for ideas, please let us know about your interest and skills and we will be happy to make a suggestion or three.

 We follow Google's C++ Style Guide~\footnote{\url{http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml}} and use \texttt{git} for version control.

 \section{Citing Ceres Solver}
 If you use Ceres for an academic publication, please cite this manual. e.g.,
 \begin{verbatim}
 @manual{ceres-manual,
 	Author = {Sameer Agarwal and Keir Mierle},
 	Title = {Ceres Solver: Tutorial \& Reference},
 	Organization = {Google Inc.}
 }
 \end{verbatim}


 \section{Acknowledgements}
 A number of people have helped with the development and open sourcing of Ceres.

 Fredrik Schaffalitzky when he was at Google started the development of Ceres, and even though much has changed since then, many of the ideas from his original design are still present in the current code.

 Amongst Ceres' users at Google two deserve special mention: William Rucklidge and James Roseborough. William was the first user of Ceres. He bravely took on the task of porting production code to an as-yet unproven optimization library, reporting bugs and helping fix them along the way. James is perhaps the most sophisticated user of Ceres at Google. He has reported and fixed bugs and helped evolve the API for the better.

 Nathan Wiegand contributed the MacOS port.
	%!TEX root = ceres-solver.tex
	\chapter{Introduction}
	\label{chapter:introduction}
	Ceres Solver\footnote{For brevity, in the rest of this document we will just use the term Ceres.} is a non-linear least squares solver developed at Google. It is designed to solve small and large sparse problems accurately and efficiently~\footnote{For a gentle but brief introduction to non-liner least squares problems, please start by reading the~\hyperref[part:tutorial]{Tutorial}}. Amongst its various features is a simple but expressive API with support for automatic differentiation, robust norms, local parameterizations, automatic gradient checking, multithreading and automatic problem structure detection.

	The key computational cost when solving a non-linear least squares problem is the solution of a linear least squares problem in each iteration. To this end Ceres supports a number of different linear solvers suited for different needs. This includes dense QR factorization (using \eigen) for small scale problems, sparse Cholesky factorization (using \texttt{SuiteSparse}) for general sparse problems and specialized Schur complement based solvers for problems that arise in multi-view geometry~\cite{hartley-zisserman-book-2004}.

	Ceres has been used for solving a variety of problems in computer vision and machine learning at Google with sizes that range from a tens of variables and objective functions with a few hundred terms to problems with millions of variables and objective functions with tens of millions of terms.


	\section{What's in a name?}
	While there is some debate as to who invented of the method of Least Squares~\cite{stigler1981gauss}. There is no debate that it was Carl Friedrich Gauss's prediction of the orbit of the newly discovered asteroid Ceres based on just 41 days of observations that brought it to the attention of the world~\cite{tennenbaum-director}. We named our solver after Ceres to celebrate this seminal event in the history of astronomy, statistics and optimization.

	\section{Contributing to Ceres Solver}

	We welcome contributions to Ceres, whether they are new features, bug fixes or tests. The Ceres mailing list\footnote{\url{http://groups.google.com/group/ceres-solver}} is the best place for all development related discussions. Please consider joining it. If you have ideas on how you would like to contribute to Ceres, it is a good idea to let us know on the mailinglist before you start development. We may have suggestions that will save effort when trying to merge your work into the main branch. If you are looking for ideas, please let us know about your interest and skills and we will be happy to make a suggestion or three.

	We follow Google's C++ Style Guide~\footnote{\url{http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml}} and use \texttt{git} for version control.

	\section{Citing Ceres Solver}
	If you use Ceres for an academic publication, please cite this manual. e.g.,
	\begin{verbatim}
	@manual{ceres-manual,
	Author = {Sameer Agarwal and Keir Mierle},
	Title = {Ceres Solver: Tutorial \& Reference},
	Organization = {Google Inc.}
	}
	\end{verbatim}


	\section{Acknowledgements}
	A number of people have helped with the development and open sourcing of Ceres.

	Fredrik Schaffalitzky when he was at Google started the development of Ceres, and even though much has changed since then, many of the ideas from his original design are still present in the current code.

	Amongst Ceres' users at Google two deserve special mention: William Rucklidge and James Roseborough. William was the first user of Ceres. He bravely took on the task of porting production code to an as-yet unproven optimization library, reporting bugs and helping fix them along the way. James is perhaps the most sophisticated user of Ceres at Google. He has reported and fixed bugs and helped evolve the API for the better.

	Nathan Wiegand contributed the MacOS port.