ACM Transactions on Mathematical Software · 2009 · 193 citations · 23 references
Numerical AnalysisReduced Order ModelingEngineeringNumerical ComputationUnsymmetric Linear SystemsMatrix MethodParallel ComputingRecent AdvancesLow-rank ApproximationNumerical EvaluationIterative MethodsComputer EngineeringInverse ProblemsMatrix AnalysisNumerical Method For Partial Differential EquationSparse RepresentationMatrix FactorizationIlu-type PreconditionersMultilevel Lu Factorizations
Recent advances in multilevel LU factorizations and novel preprocessing techniques have led to an extremely large number of possibilities for preconditioning sparse, unsymmetric linear systems for solving with iterative methods. However, not all combinations work well for all systems, so making the right choices is essential for obtaining an efficient solver. The numerical results for 256 matrices presented in this article give an indication of which approaches are suitable for which matrices (based on different criteria, such as total computation time or fill-in) and of the differences between the methods.
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