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Conjugate gradient methods

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This minimum is guaranteed to exist in general only if '''A''' is symmetric positive definite. The preconditioned version of these methods use a different subspace for constructing the iterates, but it satisfies the same minimization property over different subspace. It requires that the preconditioner '''M''' is symmetric and positive definite.
This minimum is guaranteed to exist in general only if '''A''' is symmetric positive definite. The preconditioned version of these methods use a different subspace for constructing the iterates, but it satisfies the same minimization property over different subspace. It requires that the preconditioner '''M''' is symmetric and positive definite.
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==External links==
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* [http://www.math-linux.com/spip.php?article54 Conjugate Gradient Method] by N. Soualem.
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<i> Return to [[Numerical methods | Numerical Methods]] </i>
<i> Return to [[Numerical methods | Numerical Methods]] </i>

Revision as of 18:39, 19 August 2006

Basic Concept

For the system of equations:

 A \cdot X = B

The unpreconditioned conjugate gradient method constructs the ith iterate x^{(k)} as an element of  x^{(k)}  + span\left\{ {r^{(0)} ,...,A^{i - 1} r^{(0)} } \right\}  so that so that  \left( {x^{(0)}  - \hat x} \right)^T A\left( {x^{(i)}  - \hat x} \right) is minimized , where  {\hat x} is the exact solution of  AX = B .

This minimum is guaranteed to exist in general only if A is symmetric positive definite. The preconditioned version of these methods use a different subspace for constructing the iterates, but it satisfies the same minimization property over different subspace. It requires that the preconditioner M is symmetric and positive definite.

External links


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