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MCS
2008
Springer

Dynamical low-rank approximation: applications and numerical experiments

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Dynamical low-rank approximation: applications and numerical experiments
Dynamical low-rank approximation is a differential-equation based approach to efficiently computing low-rank approximations to time-dependent large data matrices or to solutions of large matrix differential equations. We illustrate its use in the following application areas: as an updating procedure in latent semantic indexing for information retrieval, in the compression of series of images, and in the solution of time-dependent partial differential equations, specifically on a blow-up problem of a reaction-diffusion equation in 2 and 3 spatial dimensions. In 3D and higher dimensions, space discretization yields a tensor differential equation whose solution is approximated by low-rank tensors, effectively solving a system of discretized partial differential equations in 1 spatial dimension.
Achim Nonnenmacher, Christian Lubich
Added 28 Dec 2010
Updated 28 Dec 2010
Type Journal
Year 2008
Where MCS
Authors Achim Nonnenmacher, Christian Lubich
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