Results 1 to 10 of about 51,509 (166)
Algorithms for the Rational Approximation of Matrix-Valued Functions [PDF]
SIAM Journal of Scientific Computing, 2021 A selection of algorithms for the rational approximation of matrix-valued functions are discussed, including variants of the interpolatory AAA method, the RKFIT method based on approximate least squares fitting, vector fitting, and a method based on low-rank approximation of a block Loewner matrix. A new method, called the block-AAA algorithm, based on
Ion VÍCTOR Gosea, Stefan Güttel
exaly +5 more sources
Efficient algorithms for approximating quantum partition functions [PDF]
Journal of Mathematical Physics, 2021 We establish a polynomial-time approximation algorithm for partition functions of quantum spin models at high temperature. Our algorithm is based on the quantum cluster expansion of Netočný and Redig and the cluster expansion approach to designing algorithms due to Helmuth, Perkins, and Regts.
Ryan L. Mann, Tyler Helmuth
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A simple algorithm for approximation by nomographic functions [PDF]
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 6 pages, 7 figures, 2 tables.
Steffen Limmer +2 more
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Mathematical models and soil fertility management software [PDF]
E3S Web of Conferences, 2020 The article presents the results of studies on parametric approximation in spaces R2 (functions of one variable), R3 (functions of two variables) and Rn(n>3) (functions of three or more variables).
Mitrofanov Sergey +3 more
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The AAAtrig Algorithm for Rational Approximation of Periodic Functions [PDF]
SIAM Journal on Scientific Computing, 2021 We present an extension of the AAA (adaptive Antoulas--Anderson) algorithm for periodic functions, called 'AAAtrig'. The algorithm uses the key steps of AAA approximation by (i) representing the approximant in (trigonometric) barycentric form and (ii) selecting the support points greedily.
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Algorithmic Survey of Parametric Value Function Approximation [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2013 Reinforcement learning (RL) is a machine learning answer to the optimal control problem. It consists of learning an optimal control policy through interactions with the system to be controlled, the quality of this policy being quantified by the so-called value function.
Geist, Matthieu, Pietquin, Olivier
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Approximation of Hysteresis Changes in Electrical Steel Sheets
Energies, 2021 This paper describes a simple method of approximating hysteresis changes in electrical steel sheets. This method is based on assumptions that flux density or field strength changes are a sum or a difference of functions that describe one curve of the ...
Witold Mazgaj +2 more
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An Algorithm for Best Generalised Rational Approximation of Continuous Functions [PDF]
Set-Valued and Variational Analysis, 2022 The motivation of this paper is the development of an optimisation method for solving optimisation problems appearing in Chebyshev rational and generalised rational approximation problems, where the approximations are constructed as ratios of linear forms (linear combinations of basis functions).
R. Díaz Millán +2 more
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Journal of Open Research Software, 2022 Function approximation, integration, and optimization are three fundamental mathematical problems. They are especially challenging when the functions involved fluctuate wildly in certain parts of the domain, or if the domain is high dimensional. Ideally,
Xin Tong +9 more
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Data in Brief, 2019 This paper presents the data of multimodal functions that emulate the performance of an array of five photovoltaic modules under partial shading conditions.
Carlos Robles-Algarín +2 more
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