70 lines
2.7 KiB
Plaintext
70 lines
2.7 KiB
Plaintext
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namespace Eigen {
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/** \eigenManualPage LeastSquares Solving linear least squares systems
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This page describes how to solve linear least squares systems using %Eigen. An overdetermined system
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of equations, say \a Ax = \a b, has no solutions. In this case, it makes sense to search for the
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vector \a x which is closest to being a solution, in the sense that the difference \a Ax - \a b is
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as small as possible. This \a x is called the least square solution (if the Euclidean norm is used).
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The three methods discussed on this page are the SVD decomposition, the QR decomposition and normal
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equations. Of these, the SVD decomposition is generally the most accurate but the slowest, normal
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equations is the fastest but least accurate, and the QR decomposition is in between.
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\eigenAutoToc
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\section LeastSquaresSVD Using the SVD decomposition
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The \link JacobiSVD::solve() solve() \endlink method in the JacobiSVD class can be directly used to
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solve linear squares systems. It is not enough to compute only the singular values (the default for
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this class); you also need the singular vectors but the thin SVD decomposition suffices for
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computing least squares solutions:
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<table class="example">
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<tr><th>Example:</th><th>Output:</th></tr>
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<tr>
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<td>\include TutorialLinAlgSVDSolve.cpp </td>
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<td>\verbinclude TutorialLinAlgSVDSolve.out </td>
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</tr>
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</table>
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This is example from the page \link TutorialLinearAlgebra Linear algebra and decompositions \endlink.
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\section LeastSquaresQR Using the QR decomposition
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The solve() method in QR decomposition classes also computes the least squares solution. There are
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three QR decomposition classes: HouseholderQR (no pivoting, so fast but unstable),
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ColPivHouseholderQR (column pivoting, thus a bit slower but more accurate) and FullPivHouseholderQR
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(full pivoting, so slowest and most stable). Here is an example with column pivoting:
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<table class="example">
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<tr><th>Example:</th><th>Output:</th></tr>
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<tr>
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<td>\include LeastSquaresQR.cpp </td>
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<td>\verbinclude LeastSquaresQR.out </td>
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</tr>
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</table>
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\section LeastSquaresNormalEquations Using normal equations
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Finding the least squares solution of \a Ax = \a b is equivalent to solving the normal equation
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<i>A</i><sup>T</sup><i>Ax</i> = <i>A</i><sup>T</sup><i>b</i>. This leads to the following code
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<table class="example">
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<tr><th>Example:</th><th>Output:</th></tr>
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<tr>
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<td>\include LeastSquaresNormalEquations.cpp </td>
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<td>\verbinclude LeastSquaresNormalEquations.out </td>
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</tr>
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</table>
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If the matrix \a A is ill-conditioned, then this is not a good method, because the condition number
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of <i>A</i><sup>T</sup><i>A</i> is the square of the condition number of \a A. This means that you
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lose twice as many digits using normal equation than if you use the other methods.
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*/
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}
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