Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Daniel Kressner"'
Autor:
Daniel Kressner, Christoph Strössner
Publikováno v:
SIAM Journal on Imaging Sciences. 16:169-191
By adding entropic regularization, multi-marginal optimal transport problems can be transformed into tensor scaling problems, which can be solved numerically using the multi-marginal Sinkhorn algorithm. The main computational bottleneck of this algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1d595af8f1d5e239a400df161f30545
https://doi.org/10.1137/22m1478355
https://doi.org/10.1137/22m1478355
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 43:151-177
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abe12b68a713af212629cfdcd833e94a
https://doi.org/10.1137/21m1432594
https://doi.org/10.1137/21m1432594
Autor:
Christoph Strössner, Daniel Kressner
Publikováno v:
IMA Journal of Numerical Analysis.
Global spectral methods offer the potential to compute solutions of partial differential equations numerically to very high accuracy. In this work, we develop a novel global spectral method for linear partial differential equations on cubes by extend
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::155c6659a090e51f8d3475f3c0b0fd87
https://doi.org/10.1093/imanum/drac030
https://doi.org/10.1093/imanum/drac030
Publikováno v:
Numerical Linear Algebra with Applications. 29
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5aa37057a31fe657bf5b8670fe006366
https://doi.org/10.1002/nla.2448
https://doi.org/10.1002/nla.2448
The Schur decomposition of a square matrix $A$ is an important intermediate step of state-of-the-art numerical algorithms for addressing eigenvalue problems, matrix functions, and matrix equations. This work is concerned with the following task: Comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a546820ab14a653c5db34aba229f5f5b
https://www.bib.irb.hr/1196442
https://www.bib.irb.hr/1196442
This paper is concerned with two improved variants of the Hutch++ algorithm for estimating the trace of a square matrix, implicitly given through matrix-vector products. Hutch++ combines randomized low-rank approximation in a first phase with stochas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f271bb2aba521050af748687160d1590
http://arxiv.org/abs/2109.10659
http://arxiv.org/abs/2109.10659
Publikováno v:
Advances in Computational Mathematics. 47
In this article, a new method is proposed to approximate the rightmost eigenpair of certain matrix-valued linear operators, in a low-rank setting. First, we introduce a suitable ordinary differential equation, whose solution allows us to approximate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35cd5d4345a22cc634da3d41d70b8afc
https://doi.org/10.1007/s10444-021-09895-2
https://doi.org/10.1007/s10444-021-09895-2
Autor:
Daniel Kressner, Ana Susnjara
Publikováno v:
Numerical Linear Algebra with Applications. 28
Based on the spectral divide-and-conquer algorithm by Nakatsukasa and Higham [SIAM J. Sci. Comput., 35(3): A1325-A1349, 2013], we propose a new algorithm for computing all the eigenvalues and eigenvectors of a symmetric banded matrix. For this purpos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12e4c60d62e2958f2a3f077f83af7765
https://doi.org/10.1002/nla.2365
https://doi.org/10.1002/nla.2365
Publikováno v:
Numerical Linear Algebra and Applications
Numerical Linear Algebra with Applications, 28(1):e2339. Wiley
Numerical Linear Algebra with Applications, 28(1):e2339. Wiley
Block Krylov subspace methods (KSMs) comprise building blocks in many state-of-the-art solvers for large-scale matrix equations as they arise, for example, from the discretization of partial differential equations. While extended and rational block K
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c8312968aba68133d1eafcfb1c5c86b
https://hdl.handle.net/21.11116/0000-0007-8EE7-521.11116/0000-0005-D5BC-721.11116/0000-0005-D5BE-5
https://hdl.handle.net/21.11116/0000-0007-8EE7-521.11116/0000-0005-D5BC-721.11116/0000-0005-D5BE-5
Numerical continuation in the context of optimization can be used to mitigate convergence issues due to a poor initial guess. In this work, we extend this idea to Riemannian optimization problems, that is, the minimization of a target function on a R
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::910d216ad15379046a16ad78d259458f