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Wim Vanroose

U. Antwerpen

9 papers hereh-index 171.1k citations100 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author1
  • last author7

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • math.NA4
  • cs.DC1
  • math.DS1
  • math.OC1
  • math-ph1
  • physics.comp-ph1
affiliations
  • U. Antwerpen
  • Motulus CVBA
Homepage
same name
  • Wim Vanroose — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20062021
most citedApplying numerical continuation to the parameter dependence of solutions of the Schrödinger equation

5 citations · 9 across the 5 of their papers we have counts for

collaborators
Showing math.NAShow all

4 papers · 1 filter

math.NA2021

Sequential Projected Newton method for regularization of nonlinear least squares problems

Jeffrey Cornelis, Wim Vanroose

We develop a computationally efficient algorithm for the automatic regularization of nonlinear inverse problems based on the discrepancy principle. We formulate the problem as an e…

math.NA2021

Krylov-Simplex method that minimizes the residual in ℓ1​-norm or ℓ∞​-norm

Wim Vanroose, Jeffrey Cornelis

The paper presents two variants of a Krylov-Simplex iterative method that combines Krylov and simplex iterations to minimize the residual r=b−Ax. The first method minimizes $\|…

math.NA2020

Projected Newton method for noise constrained ℓp​ regularization

Jeffrey Cornelis, Wim Vanroose

Choosing an appropriate regularization term is necessary to obtain a meaningful solution to an ill-posed linear inverse problem contaminated with measurement errors or noise. The $…

math.NA2019

Projected Newton Method for noise constrained Tikhonov regularization

Jeffrey Cornelis, Nick Schenkels, Wim Vanroose

Tikhonov regularization is a popular approach to obtain a meaningful solution for ill-conditioned linear least squares problems. A relatively simple way of choosing a good regulari…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.