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. | ||
- | + | ==External links== | |
+ | * [http://www.math-linux.com/spip.php?article54 Conjugate Gradient Method] by N. Soualem. | ||
+ | * [http://www.math-linux.com/spip.php?article55 Preconditioned 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> |
Latest revision as of 17:49, 26 August 2006
Basic Concept
For the system of equations:
The unpreconditioned conjugate gradient method constructs the ith iterate as an element of so that so that is minimized , where is the exact solution of .
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
- Conjugate Gradient Method by N. Soualem.
- Preconditioned Conjugate Gradient Method by N. Soualem.
Return to Numerical Methods