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    Strong-stability-preserving additive linear multistep methods

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    1601.03637.pdf
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    Type
    Article
    Authors
    Hadjimichael, Yiannis cc
    Ketcheson, David I. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Applied Mathematics and Computational Science Program
    Date
    2018-02-20
    Permanent link to this record
    http://hdl.handle.net/10754/627959
    
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    Abstract
    The analysis of strong-stability-preserving (SSP) linear multistep methods is extended to semi-discretized problems for which different terms on the right-hand side satisfy different forward Euler (or circle) conditions. Optimal perturbed and additive monotonicity-preserving linear multistep methods are studied in the context of such problems. Optimal perturbed methods attain larger monotonicity-preserving step sizes when the different forward Euler conditions are taken into account. On the other hand, we show that optimal SSP additive methods achieve a monotonicity-preserving step-size restriction no better than that of the corresponding nonadditive SSP linear multistep methods.
    Citation
    Hadjimichael Y, Ketcheson DI (2018) Strong-stability-preserving additive linear multistep methods. Mathematics of Computation 87: 2295–2320. Available: http://dx.doi.org/10.1090/mcom/3296.
    Sponsors
    The authors would like to thank the anonymous referees for their suggestions that significantly improved the paper. Also, they would like to thank Lajos Loczi and Inmaculada Higueras for carefully reading this manuscript and making valuable comments.
    Publisher
    American Mathematical Society (AMS)
    Journal
    Mathematics of Computation
    DOI
    10.1090/mcom/3296
    arXiv
    1601.03637
    Additional Links
    http://www.ams.org/journals/mcom/2018-87-313/S0025-5718-2018-03296-3/
    ae974a485f413a2113503eed53cd6c53
    10.1090/mcom/3296
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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