Geometric Methods on Low-Rank Matrix and Tensor Manifolds
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Publication:3300541
DOI10.1007/978-3-030-31351-7_9OpenAlexW3014181683MaRDI QIDQ3300541
Bart Vandereycken, André Uschmajew
Publication date: 29 July 2020
Published in: Handbook of Variational Methods for Nonlinear Geometric Data (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-31351-7_9
Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)
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Dynamically Orthogonal Runge–Kutta Schemes with Perturbative Retractions for the Dynamical Low-Rank Approximation, Low-rank tensor methods for partial differential equations, Streaming Tensor Train Approximation, Geometry of tree-based tensor formats in tensor Banach spaces, Low-rank nonnegative tensor approximation via alternating projections and sketching, Matrix completion with sparse measurement errors, Riemannian thresholding methods for row-sparse and low-rank matrix recovery, Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats, An Equivalence between Critical Points for Rank Constraints Versus Low-Rank Factorizations, Computing low-rank rightmost eigenpairs of a class of matrix-valued linear operators, Riemannian gradient descent methods for graph-regularized matrix completion, A Riemannian rank-adaptive method for low-rank matrix completion, Existence of dynamical low-rank approximations to parabolic problems, Riemannian Multigrid Line Search for Low-Rank Problems
Uses Software
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