Computation of Optimal Monotonicity Preserving General Linear Methods

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Monotonicity preserving numerical methods for ordinary differential equations prevent the growth of propagated errors and preserve convex boundedness properties of the solution. We formulate the problem of finding optimal monotonicity preserving general linear methods for linear autonomous equations, and propose an efficient algorithm for its solution. This algorithm reliably finds optimal methods even among classes involving very high order accuracy and that use many steps and/or stages. The optimality of some recently proposed methods is verified, and many more efficient methods are found. We use similar algorithms to find optimal strong stability preserving linear multistep methods of both explicit and implicit type, including methods for hyperbolic PDEs that use downwind-biased operators.
Original languageEnglish (US)
Pages (from-to)1497-1513
Number of pages17
JournalMathematics of Computation
Issue number267
StatePublished - Apr 27 2009
Externally publishedYes

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KAUST Repository Item: Exported on 2020-10-01


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