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 methods discussed on this page are the complete orthogonal decomposition (COD), the SVD
decomposition, other QR decompositions, and normal equations. For most problems, we recommend
CompleteOrthogonalDecomposition: it robustly computes the minimum-norm least squares solution
(like the SVD) for both over- and under-determined systems, including rank-deficient ones, but at
QR-like speed. For large problems, RandCompleteOrthogonalDecomposition computes the same kind of
factorization with a randomized blocked rank-revealing QR. The SVD is the most robust but also the
slowest; use it when you also need singular values or vectors. Normal equations are the fastest but
least robust.

\eigenAutoToc


\section LeastSquaresCOD Using the complete orthogonal decomposition (recommended)

CompleteOrthogonalDecomposition is the recommended method for least squares problems. It handles the
widest class of problems — overdetermined, underdetermined, and rank-deficient systems — and computes
the minimum-norm solution when the system is rank-deficient or underdetermined, just like the SVD.
It is based on a rank-revealing QR factorization (ColPivHouseholderQR) followed by a post-processing
step, so it is significantly faster than SVD while providing comparable robustness.

<table class="example">
<tr><th>Example:</th><th>Output:</th></tr>
<tr>
  <td>\include LeastSquaresCOD.cpp </td>
  <td>\verbinclude LeastSquaresCOD.out </td>
</tr>
</table>

For large problems, RandCompleteOrthogonalDecomposition computes the same kind of factorization
through a randomized blocked rank-revealing QR (RandColPivHouseholderQR) instead. It selects each
block of pivots from a small Gaussian sketch of the matrix, which keeps nearly all of the work in
level-3 BLAS kernels rather than in the memory-bound column scan that classical column pivoting
requires. The pivots therefore differ from the ones classical column pivoting would choose, and so
do the resulting factors, but the pivot-quality difference is empirically minor. It has the same interface, so it is a drop-in
replacement in the example above, and additionally exposes setBlockSize() and setSeed() to control
the underlying QR. For small matrices the cost of forming the sketch dominates, so prefer
CompleteOrthogonalDecomposition there.

Both classes also provide pseudoInverse(); they are the only decompositions in %Eigen that do. Note
that solve() is more efficient and more accurate than multiplying by an explicitly formed
pseudo-inverse.


\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.
The SVD gives you singular values and vectors in addition to the least squares solution, but if you
only need the solution, CompleteOrthogonalDecomposition (above) is faster.


\section LeastSquaresQR Using other QR decompositions

The solve() method in QR decomposition classes also computes the least squares solution. Besides the
two complete orthogonal decompositions (above), there are four other QR decomposition classes:
HouseholderQR (no pivoting, so fast but unreliable if your matrix is not full rank),
ColPivHouseholderQR (column pivoting, a bit slower but rank-revealing), RandColPivHouseholderQR
(randomized blocked column pivoting, rank-revealing and intended for large matrices), and
FullPivHouseholderQR (full pivoting, significantly slower and rarely needed in practice).
Note that only the complete orthogonal decompositions and the SVD-based solvers compute minimum-norm
solutions for rank-deficient or underdetermined problems; the plain QR variants do not.
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.

*/

}