Numerical analysis Iterative methods for linear systems Concept
Iterative method
Summary
An iterative method solves a problem by producing a sequence of approximate solutions, each computed from the previous ones, that converges to a solution. It contrasts with a direct method, which gives the answer after a fixed finite number of steps. Iterative methods are the standard tool for nonlinear equations and for very large sparse linear systems, where direct elimination costs too much.
Where this leads
17 pages build on this one.
- Derivation of the conjugate gradient method
- Conjugate residual method
- Minimal residual method
- Successive over-relaxation
- Modified Richardson iteration
- Schwarz alternating method
- Generalized minimal residual method
- Multigrid method
- Conjugate gradient method
- Jacobi method
- Kaczmarz method
- Gauss–Seidel method
- Biconjugate gradient method
- Biconjugate gradient stabilized method
- Chebyshev iteration
- Domain decomposition methods
- Symmetric successive over-relaxation
Further reading
About this page
| Tag | 2RPE77ACER |
|---|---|
| URI | m2://2RPE77ACER |
| Kind | concept |
| Writing | W0 stub |
| MSC | 65F10, 65H10 |
| Same as |
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Results about this
- Conjugate residual method
- Minimal residual method
- Successive over-relaxation
- Modified Richardson iteration
- Generalized minimal residual method
- Multigrid method
- Conjugate gradient method
- Jacobi method
- Kaczmarz method
- Gauss–Seidel method
- Biconjugate gradient method
- Biconjugate gradient stabilized method
- Chebyshev iteration
Used by
- Conjugate residual method
- Minimal residual method
- Successive over-relaxation
- Modified Richardson iteration
- Schwarz alternating method
- Generalized minimal residual method
- Multigrid method
- Conjugate gradient method
- Jacobi method
- Kaczmarz method
- Gauss–Seidel method
- Biconjugate gradient method
- Biconjugate gradient stabilized method
- Chebyshev iteration
- Domain decomposition methods
- Symmetric successive over-relaxation