More Numerically Accurate Algorithm for Stiff Matrix Exponential

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@Article{lazebnik:2024:Mathematics,

• author = "Teddy Lazebnik and Svetlana Bunimovich-Mendrazitsky",
• title = "More Numerically Accurate Algorithm for Stiff Matrix Exponential",
• journal = "Mathematics",
• year = "2024",
• volume = "12",
• number = "8",
• pages = "Article No. 1151",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-7390",
• URL = "https://www.mdpi.com/2227-7390/12/8/1151",
• DOI = "doi:10.3390/math12081151",
• abstract = "In this paper, we propose a novel, highly accurate numerical algorithm for matrix exponentials (MEs). The algorithm is based on approximating Putzer's algorithm by analytically solving the ordinary differential equation (ODE)-based coefficients and approximating them. We show that the algorithm outperforms other ME algorithms for stiff matrices for several matrix sizes while keeping the computation and memory consumption asymptotically similar to these algorithms. In addition, we propose a numerical-error- and complexity-optimised decision tree model for efficient ME computation based on machine learning and genetic programming methods. We show that, while there is not one ME algorithm that outperforms the others, one can find a good algorithm for any given matrix according to its properties.",
• notes = "also known as \cite{math12081151}",

}

Genetic Programming entries for Teddy Lazebnik Svetlana Bunimovich-Mendrazitsky

Citations