Výsledky vyhledávání - Multivariate function approximation

Newton–Padé approximations for multivariate functions Autor Akal, C., Lukashov, A.

“…We modify Cuyt and Verdonk’s approach to multivariate Newton–Padé approximations. Explicit formulas are given for coefficients which can be computed once…”
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Constructing Embedded Lattice-Based Algorithms for Multivariate Function Approximation with a Composite Number of Points Autor Kuo, Frances Y., Mo, Weiwen, Nuyens, Dirk

“…We approximate d -variate periodic functions in weighted Korobov spaces with general weight parameters using n function values at lattice points…”
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Reinforcement learning control with function approximation via multivariate simplex splines Autor Feng, Yiting, Zhou, Ye, Ho, Hann Woei, Mat Isa, Nor Ashidi

“…Summary In the field of optimal control for continuous nonlinear systems, function approximation methods are often employed to overcome the curse of dimensionality…”
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A Positive Semidefinite Safe Approximation of Multivariate Distributionally Robust Constraints Determined by Simple Functions: A Positive Semidefinite Safe Approximation of Multivariate Autor Dienstbier, Jana, Liers, Frauke, Rolfes, Jan

“… Based on such a semi-infinite reformulation, we present a safe approximation that allows for the computation of feasible solutions for DROs that depend on nonconvex multivariate simple functions…”
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Multivariate function approximations using the D-MORPH algorithm Autor Chakraborty, Souvik, Chowdhury, Rajib

“… In this study, we propose a new method for approximating multivariate functions based on the diffeomorphic modulation under observable response preserving homotopy (D-MORPH) algorithm…”
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Adaptive Cross Approximation of Multivariate Functions Autor Bebendorf, M.

“…In this article we present and analyze a new scheme for the approximation of multivariate functions ( d =3,4…”
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Faster approximation to multivariate functions by combined Bernstein-Taylor operators Autor Duman, Oktay

“…In this article, we incorporate multivariate Taylor polynomials into the definition of the Bernstein operators to get a faster approximation to multivariate functions by these combined operators…”
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Constructive Approximation to Multivariate Function by Decay RBF Neural Network Autor Hou, Muzhou, Han, Xuli

“… + 1 hidden neurons can interpolate n + 1 multivariate samples with zero error. Then we prove that the given decay RBFs can uniformly approximate any continuous multivariate functions with arbitrary precision without training…”
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A novel piecewise multivariate function approximation method via universal matrix representation Autor Tuna, Süha, Tunga, Burcu

“… This method brings up a finite expansion to represent a multivariate function in terms of less-variate functions with the assistance of univariate support functions…”
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Approximation of multivariate function by using new multivariate Bernstein α-polynomials Autor Hou, Zai-En, Zhang, Ke-Cun

“…In this paper the approximation of multivariate function by using multivariate Bernstein α…”
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Shape preserving approximation for unbounded multivariate functions Autor Kareem, Mayada Ali, Kamel, Rehab Amer, Hussain, Ahmed Hadi

“…The purpose of this research is to present a new method for creating polynomials and spline approximation to unbounded multivariate functions in L ρ , β ([-1, 1…”
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Approximation of multivariate periodic functions by trigonometric polynomials based on rank-1 lattice sampling Autor Kämmerer, Lutz, Potts, Daniel, Volkmer, Toni

“…In this paper, we present algorithms for the approximation of multivariate periodic functions by trigonometric polynomials…”
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Complex-Valued Multivariate Neural Network (MNN) Approximation by Parameterized Half-Hyperbolic Tangent Function Autor Karateke, Seda

“…This paper deals with a family of normalized multivariate neural network (MNN) operators of complex-valued continuous functions for a multivariate context on a box of RN¯, N¯∈N…”
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Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions Autor Saibaba, Arvind K., Minster, Rachel, Kilmer, Misha E.

“…In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tucker format…”
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Low dimensional approximation and generalization of multivariate functions on smooth manifolds using deep ReLU neural networks Autor Labate, Demetrio, Shi, Ji

“…The expressive power of deep neural networks is manifested by their remarkable ability to approximate multivariate functions in a way that appears to overcome the curse of dimensionality…”
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Solving numerically nonlinear systems of balance laws by multivariate sigmoidal functions approximation Autor Costarelli, Danilo, Spigler, Renato

“…A numerical method for solving systems of nonlinear one-dimensional balance laws, based on multivariate sigmoidal functions approximation, is developed…”
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Nonlinear trigonometric approximations of multivariate function classes Autor Bazarkhanov, D. B.

“…Order-sharp estimates are established for the best N -term approximations of functions from Nikol’skii…”
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Representation and approximation of multivariate functions with mixed smoothness by hyperbolic wavelets Autor Heping, Wang

“…In this paper, we study the representation theorems of multivariate functions with mixed smoothness by wavelet basis formed by tensor products of univariate wavelets, we also study the best approximation in the L q( R d…”
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Exponential approximation of multivariate bandlimited functions from average oversampling Autor Chen, Wenjian, Zhang, Haizhang

“… Such a sampling strategy is more practical and more stable. In this note, we present an explicit method with an exponentially-decaying approximation error to reconstruct a multivariate bandlimited function from its finite average oversampling data…”
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Optimal Order of Convergence and (In)Tractability of Multivariate Approximation of Smooth Functions Autor Novak, Erich, Woźniakowski, Henryk

“…We study the approximation problem for C ∞ functions f :[0,1] d →ℝ with respect to a W p m -norm. Here, m =[ m , m ,…, m…”
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