Titlebook: Sparse Grids and Applications - Miami 2016; Jochen Garcke,Dirk Pflüger,Guannan Zhang Conference proceedings 2018 Springer International Pu

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書(shū)目名稱(chēng)Sparse Grids and Applications - Miami 2016讀者反饋學(xué)科排名

ANTH

Comparing Nested Sequences of Leja and PseudoGauss Points to Interpolate in 1D and Solve the Schroee a 9D vibrational Schroedinger equation. Collocation has the advantage that it obviates the need to compute integrals with quadrature. A multi-dimension sparse grid is built from the Leja points and Hermite-type basis functions by restricting sparse grid levels .. using ∑...(..)?≤?., where ..(..) i

chondromalacia

On the Convergence Rate of Sparse Grid Least Squares Regression,st 15 years, a thorough theoretical analysis of stability properties, error decay behavior and appropriate couplings between the dataset size and the grid size has not been provided yet. In this paper, we will present a framework which will allow us to close this gap and rigorously derive upper boun

sigmoid-colon

Multilevel Adaptive Stochastic Collocation with Dimensionality Reduction,e applications. Standard MLSC typically employs grids with predetermined resolutions. Even more, stochastic dimensionality reduction has not been considered in previous MLSC formulations. In this paper, we design an MLSC approach in terms of adaptive sparse grids for stochastic discretization and co

Reclaim

CUR

PACT

Sparse Grid Quadrature Rules Based on Conformal Mappings,e in the multidimensional setting. In one dimension, computation of an integral involving an analytic function using these transformed quadrature rules can improve the convergence rate by a factor approaching .∕2 versus classical interpolatory quadrature (Hale and Trefethen, SIAM J Numer Anal 46:930

gonioscopy

Solving Dynamic Portfolio Choice Models in Discrete Time Using Spatially Adaptive Sparse Grids,tive sparse grids. In doing so, I focus on Bellman equations used in finance, specifically to model dynamic portfolio choice over the life cycle. Since the complexity of the dynamic programming approach—and other approaches—grows exponentially in the dimension of the (continuous) state space, it suf

救護(hù)車(chē)

Adaptive Sparse Grid Construction in a Context of Local Anisotropy and Multiple Hierarchical Parentd basis functions are constructed from tensors of a one dimensional hierarchical rule. We consider four different hierarchies that are tailored towards general functions, high or low order polynomial approximation, or functions that satisfy homogeneous boundary conditions. The main advantage of the

Harrowing

,Smolyak’s Algorithm: A Powerful Black Box for the Acceleration of Scientific Computations, on multidimensional integration and interpolation. Since then, it has been generalized in multiple directions and has been associated with the keywords: sparse grids, hyperbolic cross approximation, combination technique, and multilevel methods. Variants of Smolyak’s algorithm have been employed in