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Gregory Beylkin

Researcher at University of Colorado Boulder

Publications -  141
Citations -  10580

Gregory Beylkin is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Multiresolution analysis & Wavelet. The author has an hindex of 42, co-authored 140 publications receiving 9991 citations. Previous affiliations of Gregory Beylkin include New York University & University of Tennessee.

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Journal ArticleDOI

Optimization via separated representations and the canonical tensor decomposition

TL;DR: A new, quadratically convergent algorithm for finding maximum absolute value entries of tensors represented in the canonical format and it is shown how to use this algorithm to find global maxima of non-convex multivariate functions in separated form.
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Multiresolution computational chemistry

TL;DR: In this article, Fann summarizes the major accomplishments in computational chemistry which represent the first substantial application of most of these new ideas in three and higher dimensions, including basis set limit computation with linear scaling for Hartree-Fock and Density Functional Theory with a wide variety of functionals including hybrid and asymptotically corrected forms.
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On generalized gaussian quadratures for bandlimited exponentials

TL;DR: This approach also yields generalized Gaussian quadratures for exponentials integrated against a non-sign-definite weight function and compute quadrature weights via l 2 and l ∞ minimization and compare the corresponding quadRature errors.
Proceedings ArticleDOI

SAR imaging and multiresolution analysis

TL;DR: In this paper, the unequally spaced Fourier transform (USFFT) was applied to SAR image formation and compared with a conventional algorithm, and the USFFT algorithm allowed comparable accuracy to traditional approaches at a slightly reduced computational cost.
Journal Article

Adaptive solution of multidimensional PDEs via tensor product wavelet decomposition

TL;DR: An adaptive multidimensional algorithm for solving strictly elliptic PDEs is described and a diagonally preconditioned “constrained” conjugate gradient (CG) method is used to compute the actual coefficients of the approximate solution.