Guaranteed a posteriori error bounds for low-rank tensor approximate solutions

Sergey Dolgov, Tomáš Vejchodský

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a guaranteed and fully computable upper bound on the energy norm of the error in low-rank tensor train (TT) approximate solutions of (possibly) high-dimensional reaction–diffusion problems. The error bound is obtained from Euler–Lagrange equations for a complementary flux reconstruction problem, which are solved in the low-rank TT representation using the block alternating linear scheme. This bound is guaranteed to be above the energy norm of the total error, including the discretization error, the tensor approximation error and the error in the solver of linear algebraic equations, although quadrature errors, in general, can pollute its evaluation. Numerical examples with the Poisson equation and the Schrödinger equation with the Henon–Heiles potential in up to 40 dimensions are presented to illustrate the efficiency of this approach.
Original languageEnglish
Article numberdraa010
Pages (from-to)1240–1266
JournalIMA Journal of Numerical Analysis
Volume41
Issue number2
DOIs
Publication statusPublished - 9 Jun 2020

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