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Mathematics

D-Index
30
Citations
4467
World Ranking
3481
National Ranking
29

Overview

Lars Eldén is affiliated with Linköping University in Sweden and works primarily in the fields of mathematics, computer science, and engineering. Their research focus includes computational mathematics, computational theory and mathematics, and computational mechanics, with additional interests in geometry and topology as well as electrical and electronic engineering.

The main topics of research covered by Lars Eldén's work include tensor decomposition and applications, matrix theory and algorithms, sparse and compressive sensing techniques, graph theory and applications, VLSI and FPGA design techniques, graph theory and algorithms, and algorithms and data compression.

Frequent publication venues for Eldén include:

  • arXiv (Cornell University)
  • Numerical Algorithms
  • Numerical Linear Algebra with Applications
  • SIAM Journal on Matrix Analysis and Applications

Collaborative work has been conducted notably with Maryam Dehghan, with whom Eldén has co-authored multiple papers.

Representative recent papers by Lars Eldén are:

  • A Krylov-Schur-like method for computing the best rank-(r1,r2,r3) approximation of large and sparse tensors, 2022, Numerical Algorithms
  • Spectral partitioning of large and sparse 3-tensors using low-rank tensor approximation, 2022, Numerical Linear Algebra with Applications
  • Multiway Spectral Graph Partitioning: Cut Functions, Cheeger Inequalities, and a Simple Algorithm, 2024, SIAM Journal on Matrix Analysis and Applications
  • Analyzing Large and Sparse Tensor Data using Spectral Low-Rank Approximation, 2020, arXiv (Cornell University)
  • Spectral Partitioning of Large and Sparse Tensors using Low-Rank Tensor Approximation, 2020, arXiv (Cornell University)

Best Publications

  • Handwritten digit classification using higher order singular value decomposition

    Berkant Savas;Lars Eldén

  • Algorithms for the regularization of ill-conditioned least squares problems

    Lars Eldén

  • Matrix methods in data mining and pattern recognition

    Lars Eldén

  • Wavelet and Fourier Methods for Solving the Sideways Heat Equation

    Lars Eldén;Fredrik Berntsson;Teresa Reginska

  • A weighted pseudoinverse, generalized singular values, and constrained least squares problems

    Lars Eldén

  • A Newton-Grassmann Method for Computing the Best Multilinear Rank-$(r_1,$ $r_2,$ $r_3)$ Approximation of a Tensor

    Lars Eldén;Berkant Savas

  • A Procrustes problem on the Stiefel manifold

    Lars Eldén;Haesun Park

  • Inexact Rayleigh Quotient-Type Methods for Eigenvalue Computations

    Simoncini;Lars Elden

  • Numerical solution of a Cauchy problem for the Laplace equation

    Fredrik Berntsson;Lars Eldén

  • Numerical solution of the sideways heat equation by difference approximation in time

    L Elden

  • Perturbation Theory for the Least Squares Problem with Linear Equality Constraints

    Lars Eldén

  • A note on the computation of the generalized cross-validation function for ill-conditioned least squares problems

    Lars Eldén

  • Solving the sideways heat equation by a wavelet - Galerkin method

    Teresa Reginska;Lars Eldén

  • Partial least-squares vs. Lanczos bidiagonalization—I: analysis of a projection method for multiple regression

    Lars Eldén

  • Approximations for a Cauchy problem for the heat equation

    L Elden

  • An 'optimal filtering' method for the sideways heat equation

    T I Seidman;L Elden

  • Numerical Solution of First-Kind Volterra Equations by Sequential Tikhonov Regularization

    Patricia K. Lamm;Lars Eldén

  • Accurate Downdating of Least Squares Solutions

    A. Bjorck;H. Park;L. Elden

  • Adaptive Eigenvalue Computations Using Newton's Method on the Grassmann Manifold

    Eva Lundström;Lars Eldén

  • Numerical linear algebra in data mining

    Lars Eldén

Frequent Co-Authors

Haesun Park
Haesun Park Georgia Institute of Technology
Valeria Simoncini
Valeria Simoncini University of Bologna
Robert Schreiber
Robert Schreiber Cerebras Systems
Dianne P. O'Leary
Dianne P. O'Leary University of Maryland, College Park
Anders Ynnerman
Anders Ynnerman Linköping University

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