A literature survey of low‐rank tensor approximation techniques
L Grasedyck, D Kressner, C Tobler - GAMM‐Mitteilungen, 2013 - Wiley Online Library
During the last years, low‐rank tensor approximation has been established as a new tool in
scientific computing to address large‐scale linear and multilinear algebra problems, which …
scientific computing to address large‐scale linear and multilinear algebra problems, which …
Model order reduction for linear and nonlinear systems: a system-theoretic perspective
In the past decades, Model Order Reduction (MOR) has demonstrated its robustness and
wide applicability for simulating large-scale mathematical models in engineering and the …
wide applicability for simulating large-scale mathematical models in engineering and the …
Computational methods for linear matrix equations
V Simoncini - siam REVIEW, 2016 - SIAM
Given the square matrices A,B,D,E and the matrix C of conforming dimensions, we consider
the linear matrix equation A\mathbfXE+D\mathbfXB=C in the unknown matrix \mathbfX. Our …
the linear matrix equation A\mathbfXE+D\mathbfXB=C in the unknown matrix \mathbfX. Our …
Low-rank matrix completion by Riemannian optimization
B Vandereycken - SIAM Journal on Optimization, 2013 - SIAM
The matrix completion problem consists of finding or approximating a low-rank matrix based
on a few samples of this matrix. We propose a new algorithm for matrix completion that …
on a few samples of this matrix. We propose a new algorithm for matrix completion that …
[PDF][PDF] Introduction to compressed sensing.
In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …
applied mathematics, computer science, and electrical engineering by suggesting that it may …
Numerical solution of large and sparse continuous time algebraic matrix Riccati and Lyapunov equations: a state of the art survey
Efficient numerical algorithms for the solution of large and sparse matrix Riccati and
Lyapunov equations based on the low rank alternating directions implicit (ADI) iteration have …
Lyapunov equations based on the low rank alternating directions implicit (ADI) iteration have …
Dynamical approximation by hierarchical Tucker and tensor-train tensors
C Lubich, T Rohwedder, R Schneider… - SIAM Journal on Matrix …, 2013 - SIAM
We extend results on the dynamical low-rank approximation for the treatment of time-
dependent matrices and tensors (Koch and Lubich; see [SIAM J. Matrix Anal. Appl., 29 …
dependent matrices and tensors (Koch and Lubich; see [SIAM J. Matrix Anal. Appl., 29 …
Normalized iterative hard thresholding for matrix completion
Matrices of low rank can be uniquely determined from fewer linear measurements, or
entries, than the total number of entries in the matrix. Moreover, there is a growing literature …
entries, than the total number of entries in the matrix. Moreover, there is a growing literature …
Convergence results for projected line-search methods on varieties of low-rank matrices via Łojasiewicz inequality
R Schneider, A Uschmajew - SIAM Journal on Optimization, 2015 - SIAM
The aim of this paper is to derive convergence results for projected line-search methods on
the real-algebraic variety M_≤k of real m*n matrices of rank at most k. Such methods extend …
the real-algebraic variety M_≤k of real m*n matrices of rank at most k. Such methods extend …
The geometry of algorithms using hierarchical tensors
A Uschmajew, B Vandereycken - Linear Algebra and its Applications, 2013 - Elsevier
In this paper, the differential geometry of the novel hierarchical Tucker format for tensors is
derived. The set HT, k of tensors with fixed tree T and hierarchical rank k is shown to be a …
derived. The set HT, k of tensors with fixed tree T and hierarchical rank k is shown to be a …