From aa0db35185f7eda94eb103b6bb92630c432512e5 Mon Sep 17 00:00:00 2001 From: Jitse Niesen Date: Sat, 18 Jan 2014 01:16:17 +0000 Subject: Add doc page on computing Least Squares. --- doc/TutorialLinearAlgebra.dox | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) (limited to 'doc/TutorialLinearAlgebra.dox') diff --git a/doc/TutorialLinearAlgebra.dox b/doc/TutorialLinearAlgebra.dox index b09f3543e..e6c41fd70 100644 --- a/doc/TutorialLinearAlgebra.dox +++ b/doc/TutorialLinearAlgebra.dox @@ -167,8 +167,8 @@ Here is an example: \section TutorialLinAlgLeastsquares Least squares solving -The best way to do least squares solving is with a SVD decomposition. Eigen provides one as the JacobiSVD class, and its solve() -is doing least-squares solving. +The most accurate method to do least squares solving is with a SVD decomposition. Eigen provides one +as the JacobiSVD class, and its solve() is doing least-squares solving. Here is an example: @@ -179,9 +179,10 @@ Here is an example:
-Another way, potentially faster but less reliable, is to use a LDLT decomposition -of the normal matrix. In any case, just read any reference text on least squares, and it will be very easy for you -to implement any linear least squares computation on top of Eigen. +Another methods, potentially faster but less reliable, are to use a Cholesky decomposition of the +normal matrix or a QR decomposition. Our page on \link LeastSquares least squares solving \endlink +has more details. + \section TutorialLinAlgSeparateComputation Separating the computation from the construction -- cgit v1.2.3