Lanczos-type solvers for nonsymmetric linear systems of equations
Методы типа Ланцоша для решения несимметричных систем линейных уравнений
1997-01-01
SCID: 54.1/8c57u4xm
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Krylov subspace methodsLanczos processlook-ahead strategiesnonsymmetric linear systemsshort recurrences
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Abstract (AI)
Among the iterative methods for solving large linear systems with a sparse (or, possibly, structured) nonsymmetric matrix, those that are based on the Lanczos process feature short recurrences for the generation of the Krylov space. This means low cost and low memory requirement. This review article introduces the reader not only to the basic forms of the Lanczos process and some of the related theory, but also describes in detail a number of solvers that are based on it, including those that are considered to be the most efficient ones. Possible breakdowns of the algorithms and ways to cure them by look-ahead are also discussed.
Key Findings
1
Algorithmic breakdowns are examined, including look-ahead strategies for preventing or curing them.
2
Lanczos-based iterative solvers address large sparse or structured nonsymmetric linear systems while requiring short recurrences.
3
Short recurrences reduce both computational cost and memory requirements for Krylov subspace generation.
4
The review presents fundamental Lanczos processes, related theory, and detailed descriptions of several efficient Lanczos-type solvers.
Research Object
Lanczos-type iterative solvers for large sparse or structured nonsymmetric linear systems
Research Subject
Short-recurrence Krylov-space generation, solver efficiency in computational cost and memory usage, and breakdown avoidance through look-ahead strategies
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1997-01-01
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