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• W2963764551 abstract "A systematic mathematical framework for the study of numerical algorithms would allow comparisons, facilitate conjugacy arguments, as well as enable the discovery of improved, accelerated, data-driven algorithms. Over the course of the last century, the Koopman operator has provided a mathematical framework for the study of dynamical systems, which facilitates conjugacy arguments and can provide efficient reduced descriptions. More recently, numerical approximations of the operator have enabled the analysis of a large number of deterministic and stochastic dynamical systems in a completely data-driven, essentially equation-free pipeline. Discrete or continuous time numerical algorithms (integrators, nonlinear equation solvers, optimization algorithms) are themselves dynamical systems. In this paper, we use this insight to leverage the Koopman operator framework in the data-driven study of such algorithms and discuss benefits for analysis and acceleration of numerical computation. For algorithms acting on high-dimensional spaces by quickly contracting them towards low-dimensional manifolds, we demonstrate how basis functions adapted to the data help to construct efficient reduced representations of the operator. Our illustrative examples include the gradient descent and Nesterov optimization algorithms, as well as the Newton-Raphson algorithm." @default.
• W2963764551 created "2019-07-30" @default.
• W2963764551 creator A5036566464 @default.
• W2963764551 creator A5065128348 @default.
• W2963764551 creator A5073005530 @default.
• W2963764551 date "2020-01-01" @default.
• W2963764551 modified "2023-10-17" @default.
• W2963764551 title "On the Koopman Operator of Algorithms" @default.
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• W2963764551 doi "https://doi.org/10.1137/19m1277059" @default.
• W2963764551 hasPublicationYear "2020" @default.
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