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<title>GNU Scientific Library &ndash; Reference Manual: Modified Cholesky Decomposition</title>

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<a name="Modified-Cholesky-Decomposition"></a>
<div class="header">
<p>
Next: <a href="Tridiagonal-Decomposition-of-Real-Symmetric-Matrices.html#Tridiagonal-Decomposition-of-Real-Symmetric-Matrices" accesskey="n" rel="next">Tridiagonal Decomposition of Real Symmetric Matrices</a>, Previous: <a href="Pivoted-Cholesky-Decomposition.html#Pivoted-Cholesky-Decomposition" accesskey="p" rel="previous">Pivoted Cholesky Decomposition</a>, Up: <a href="Linear-Algebra.html#Linear-Algebra" accesskey="u" rel="up">Linear Algebra</a> &nbsp; [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
</div>
<hr>
<a name="Modified-Cholesky-Decomposition-1"></a>
<h3 class="section">14.8 Modified Cholesky Decomposition</h3>
<a name="index-Cholesky-decomposition_002c-modified"></a>
<a name="index-Modified-Cholesky-Decomposition"></a>

<p>The modified Cholesky decomposition is suitable for solving systems
<em>A x = b</em> where <em>A</em> is a symmetric indefinite matrix. Such
matrices arise in nonlinear optimization algorithms. The standard
Cholesky decomposition requires a positive definite matrix and would
fail in this case. Instead of resorting to a method like QR or SVD,
which do not take into account the symmetry of the matrix, we can
instead introduce a small perturbation to the matrix <em>A</em> to
make it positive definite, and then use a Cholesky decomposition on
the perturbed matrix. The resulting decomposition satisfies
</p>
<div class="example">
<pre class="example">P (A + E) P^T = L D L^T
</pre></div>

<p>where <em>P</em> is a permutation matrix, <em>E</em> is a diagonal
perturbation matrix, <em>L</em> is unit lower triangular, and
<em>D</em> is diagonal. If <em>A</em> is sufficiently positive
definite, then the perturbation matrix <em>E</em> will be zero
and this method is equivalent to the pivoted Cholesky algorithm.
For indefinite matrices, the perturbation matrix <em>E</em> is
computed to ensure that <em>A + E</em> is positive definite and
well conditioned.
</p>
<dl>
<dt><a name="index-gsl_005flinalg_005fmcholesky_005fdecomp"></a>Function: <em>int</em> <strong>gsl_linalg_mcholesky_decomp</strong> <em>(gsl_matrix * <var>A</var>, gsl_permutation * <var>p</var>, gsl_vector * <var>E</var>)</em></dt>
<dd><p>This function factors the symmetric, indefinite square matrix
<var>A</var> into the Modified Cholesky decomposition <em>P (A + E) P^T = L D L^T</em>.
On input, the values from the diagonal and lower-triangular part of the matrix <var>A</var> are
used to construct the factorization. On output the diagonal of the input matrix <var>A</var> stores the diagonal
elements of <em>D</em>, and the lower triangular portion of <var>A</var>
contains the matrix <em>L</em>. Since <em>L</em> has ones on its diagonal these
do not need to be explicitely stored. The upper triangular portion of <var>A</var>
is unmodified. The permutation matrix <em>P</em> is
stored in <var>p</var> on output. The diagonal perturbation matrix is stored in
<var>E</var> on output. The parameter <var>E</var> may be set to NULL if it is not
required.
</p></dd></dl>

<dl>
<dt><a name="index-gsl_005flinalg_005fmcholesky_005fsolve"></a>Function: <em>int</em> <strong>gsl_linalg_mcholesky_solve</strong> <em>(const gsl_matrix * <var>LDLT</var>, const gsl_permutation * <var>p</var>, const gsl_vector * <var>b</var>, gsl_vector * <var>x</var>)</em></dt>
<dd><p>This function solves the perturbed system <em>(A + E) x = b</em> using the Cholesky
decomposition of <em>A + E</em> held in the matrix <var>LDLT</var> and permutation
<var>p</var> which must have been previously computed by <code>gsl_linalg_mcholesky_decomp</code>.
</p></dd></dl>

<dl>
<dt><a name="index-gsl_005flinalg_005fmcholesky_005fsvx"></a>Function: <em>int</em> <strong>gsl_linalg_mcholesky_svx</strong> <em>(const gsl_matrix * <var>LDLT</var>, const gsl_permutation * <var>p</var>, gsl_vector * <var>x</var>)</em></dt>
<dd><p>This function solves the perturbed system <em>(A + E) x = b</em> in-place using the Cholesky
decomposition of <em>A + E</em> held in the matrix <var>LDLT</var> and permutation
<var>p</var> which must have been previously computed by <code>gsl_linalg_mcholesky_decomp</code>.
On input, <var>x</var> contains the right hand side vector <em>b</em> which is
replaced by the solution vector on output.
</p></dd></dl>

<dl>
<dt><a name="index-gsl_005flinalg_005fmcholesky_005frcond"></a>Function: <em>int</em> <strong>gsl_linalg_mcholesky_rcond</strong> <em>(const gsl_matrix * <var>LDLT</var>, const gsl_permutation * <var>p</var>, double * <var>rcond</var>, gsl_vector * <var>work</var>)</em></dt>
<dd><p>This function estimates the reciprocal condition number (using the 1-norm) of the perturbed matrix
<em>A + E</em>, using its pivoted Cholesky decomposition provided in <var>LDLT</var>.
The reciprocal condition number estimate, defined as <em>1 / (||A + E||_1 \cdot ||(A + E)^{-1}||_1)</em>, is stored
in <var>rcond</var>.  Additional workspace of size <em>3 N</em> is required in <var>work</var>.
</p></dd></dl>

<hr>
<div class="header">
<p>
Next: <a href="Tridiagonal-Decomposition-of-Real-Symmetric-Matrices.html#Tridiagonal-Decomposition-of-Real-Symmetric-Matrices" accesskey="n" rel="next">Tridiagonal Decomposition of Real Symmetric Matrices</a>, Previous: <a href="Pivoted-Cholesky-Decomposition.html#Pivoted-Cholesky-Decomposition" accesskey="p" rel="previous">Pivoted Cholesky Decomposition</a>, Up: <a href="Linear-Algebra.html#Linear-Algebra" accesskey="u" rel="up">Linear Algebra</a> &nbsp; [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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