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Spectral gap for jump processes by decomposition method
Frontiers of Mathematics in China, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yonghua Mao
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Spectral, Tensor and Domain Decomposition Methods for Fractional PDEs
Computational Methods in Applied Mathematics, 2022Abstract Fractional PDEs have recently found several geophysics and imaging science applications due to their nonlocal nature and their flexibility in capturing sharp transitions across interfaces. However, this nonlocality makes it challenging to design efficient solvers for such problems.
Tianyi Shi, Harbir Antil, Drew P. Kouri
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Spectral Element Methods for Eigenvalue Problems Based on Domain Decomposition
SIAM Journal on Scientific Computing, 2022The authors are concerned with efficient methods to solve elliptic PDE eigenvalue problems by applying the spectral element discretization and domain decomposition techniques. In fact, they are focusing only on the Laplacian eigenvalue problem. Thus, they provide a rough introduction to the overlapping domain decomposition method and consider two types
Wei Wang 0338, Zhimin Zhang
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Comparison of spectral decomposition methods
First Break, 2006John P. Castagna, University of Houston, and Shengjie Sun, Fusion Geophysical discuss a number of different methods for spectral decomposition before suggesting some improvements possible with their own variation of ‘matching pursuit’ decomposition. In seismic exploration, spectral decomposition refers to any method that produces a continuous time ...
J.P. Castagna, S. Sun
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Spectral estimation methods avoiding eigenvector decomposition
International Conference on Acoustics, Speech, and Signal Processing, 2002The autoregressive principal component technique uses the singular value decomposition (SVD) of an augmented dimension estimated autocorrelation matrix R to provide an accurate identification of frequencies in white noise. To avoid the eigen-decomposition of the matrix R, S.M. Kay and A.K.
Thierry Pitarque +2 more
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Spectral Methods and Domain Decomposition for Nanophotonic Applications
Proceedings of the IEEE, 2013Nanophotonic applications often involve large-scale problems with excessive demand on computational resources. We develop a domain decomposition method (DDM) to reduce computer memory and central processing unit (CPU) time requirements by combining the spectral element method (SEM) and the spectral integral method (SIM) for large-scale finite periodic ...
Ma Luo, Yun Lin, Qing Huo Liu
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Optimal Decomposition of the Domain in Spectral Methods for Wave-Like Phenomena
SIAM Journal on Scientific Computing, 2000A strategy for determining the optimal number of grid points and subdomains in a spectral method with domain decomposition is presented. The computational costs on serial computers for large numbers of grid points is balanced against the exponential convergence for spectral approximations of smooth functions.
Carl Erik Wasberg, David I. Gottlieb
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A Nonoverlapping Domain Decomposition Method for Legendre Spectral Collocation Problems
Journal of Scientific Computing, 2007This paper deals with the Legendre spectral collocation method in a non-overlapping domain decomposition version to solve Poisson's equation in an \(L\)-shaped region. The collocation equations use Gauss-Legendre nodes instead of the more usual Gauss-Legendre-Lobatto points. The problem is decoupled into several independent steps.
Bialecki, B. +3 more
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A TV-constrained decomposition method for spectral CT
SPIE Proceedings, 2017Spectral CT is attracting more and more attention in medicine, industrial nondestructive testing and security inspection field. Material decomposition is an important issue to a spectral CT to discriminate materials. Because of the spectrum overlap of energy channels, as well as the correlation of basis functions, it is well acknowledged that ...
Xiaoyue Guo, Li Zhang, Yuxiang Xing
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Quantitative comparison of three spectral decomposition methods
International Petroleum Technology Conference, 2014Abstract Several time-frequency transform methods have been used for spectral decomposition of seismic data. Comparisons between methods have been largely qualitative with a focus on their relative time and frequency resolutions. Very little has been said about the fidelity of spectral decomposition results relative
Donald C. Adams, David Markus
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