Lars Eldén
Professor emeritus of Scientific Computing
orcid.org/000000032281856X
Matrix Methods in Data Mining and Pattern Recognition
The
second edition is now available from SIAM.
Review
Recent papers
 Multiway Spectral Graph Partitioning: Cut Functions, Cheeger Inequalities, and a Simple Algorithm. SIAM J. Matrix Anal. Appl., to appear. arXiv

A KrylovSchur Like Method for Computing the Best Rank($r_1,r_2,r_3$) Approximation of Large and Sparse Tensors. Numerical Algorithms (open access) Codes
 Spectral Partitioning of Large and Sparse Tensors using LowRank Tensor Approximation, Numerical Linear Algebra Appl. (open access)
 Analyzing Large and Sparse Tensor Data using Spectral LowRank Approximation, arXiv:2012.07754. PDF

Solving bilinear tensor least squares problems and
application to Hammerstein identification,
Numer. Lin. Alg. Appl. 2019
PDF
BIBTEX
 Semisparse PCA, Psychometrika 2018
PDF
BIBTEX
 Solving an IllPosed Cauchy Problem
for a TwoDimensional Parabolic PDE with Variable Coefficients
using a Preconditioned GMRES Method, SISC 2014
PDF
BIBTEX
 Computing Frechet Derivatives in
Partial Least Squares Regresssion, LAA 2015
PDF
BIBTEX
 Computing Semantic Clusters by
Semantic Mirroring and Spectral Graph Partitioning, Mathematics in
Computer Science 2013
PDF
BIBTEX
Springer
Link
 Solving
IllPosed Linear Systems with GMRES and a Singular
Preconditioner, SIMAX 2012
PDF
BIBTEX

Perturbation Theory and Optimality Conditions for the Best Multilinear
Rank Approximation of a Tensor, SIMAX 2011,
PDF,
BIBTEX

Krylovtype methods for tensor computations I, LAA 2012
BIBTEX
Introduction to Numerical Computation
The book by Eldén, WittmeyerKoch, and BruunNielsen from 2004 is out of print but is now available here for free.
PDF
Editorinchief of BIT Numerical Mathematics 20152018
BIT
at Springer
BIT's local web page
Quotations about data analysis
"The traditional rationale within applied mathematics has been to solve
or provide insight for equations which describe some part of the
physical world. .... But there is a paradigm shift afoot. Mathematics
is used to describe data, without the benefit of an interpolating
equation or physical principle."
(J. Glimm, Bulletin of the American Mathematical Society, 47, Jan
2010, p. 127136.)
"The analysis of large data sets to provide understanding, and
ultimately knowledge, is one of the fundamental
intellectual challenges of our time. It falls to
practitioners of the mathematical sciences
(mathematics, statistics, and computer science) to
devise new methods for carrying out analysis tasks,
as well as to construct new models or paradigms for
thinking about data."
(Gunnar Carlsson and Robert Ghrist, SIAM News, April 2012).