nest-open-source / nest-cam / v366 / eigen / 9d75fdbcd2b5a2fdab03a859ddefda95ce1217ed / . / doc / LeastSquares.dox

namespace Eigen { | |

/** \eigenManualPage LeastSquares Solving linear least squares systems | |

This page describes how to solve linear least squares systems using %Eigen. An overdetermined system | |

of equations, say \a Ax = \a b, has no solutions. In this case, it makes sense to search for the | |

vector \a x which is closest to being a solution, in the sense that the difference \a Ax - \a b is | |

as small as possible. This \a x is called the least square solution (if the Euclidean norm is used). | |

The three methods discussed on this page are the SVD decomposition, the QR decomposition and normal | |

equations. Of these, the SVD decomposition is generally the most accurate but the slowest, normal | |

equations is the fastest but least accurate, and the QR decomposition is in between. | |

\eigenAutoToc | |

\section LeastSquaresSVD Using the SVD decomposition | |

The \link BDCSVD::solve() solve() \endlink method in the BDCSVD class can be directly used to | |

solve linear squares systems. It is not enough to compute only the singular values (the default for | |

this class); you also need the singular vectors but the thin SVD decomposition suffices for | |

computing least squares solutions: | |

<table class="example"> | |

<tr><th>Example:</th><th>Output:</th></tr> | |

<tr> | |

<td>\include TutorialLinAlgSVDSolve.cpp </td> | |

<td>\verbinclude TutorialLinAlgSVDSolve.out </td> | |

</tr> | |

</table> | |

This is example from the page \link TutorialLinearAlgebra Linear algebra and decompositions \endlink. | |

If you just need to solve the least squares problem, but are not interested in the SVD per se, a | |

faster alternative method is CompleteOrthogonalDecomposition. | |

\section LeastSquaresQR Using the QR decomposition | |

The solve() method in QR decomposition classes also computes the least squares solution. There are | |

three QR decomposition classes: HouseholderQR (no pivoting, fast but unstable if your matrix is | |

not rull rank), ColPivHouseholderQR (column pivoting, thus a bit slower but more stable) and | |

FullPivHouseholderQR (full pivoting, so slowest and slightly more stable than ColPivHouseholderQR). | |

Here is an example with column pivoting: | |

<table class="example"> | |

<tr><th>Example:</th><th>Output:</th></tr> | |

<tr> | |

<td>\include LeastSquaresQR.cpp </td> | |

<td>\verbinclude LeastSquaresQR.out </td> | |

</tr> | |

</table> | |

\section LeastSquaresNormalEquations Using normal equations | |

Finding the least squares solution of \a Ax = \a b is equivalent to solving the normal equation | |

<i>A</i><sup>T</sup><i>Ax</i> = <i>A</i><sup>T</sup><i>b</i>. This leads to the following code | |

<table class="example"> | |

<tr><th>Example:</th><th>Output:</th></tr> | |

<tr> | |

<td>\include LeastSquaresNormalEquations.cpp </td> | |

<td>\verbinclude LeastSquaresNormalEquations.out </td> | |

</tr> | |

</table> | |

This method is usually the fastest, especially when \a A is "tall and skinny". However, if the | |

matrix \a A is even mildly ill-conditioned, this is not a good method, because the condition number | |

of <i>A</i><sup>T</sup><i>A</i> is the square of the condition number of \a A. This means that you | |

lose roughly twice as many digits of accuracy using the normal equation, compared to the more stable | |

methods mentioned above. | |

*/ | |

} |