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FilomatVolume 35, Issue 1, 2021, Pages 239-250

Norm bounds for the inverse and error bounds for linear complementarity problems for {P1, P2}-Nekrasov matrices(Article)(Open Access)

  • Nedović, M.,
  • Cvetković, L.
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  • aDepartment for Fundamental Sciences, Faculty of Technical Sciences, University of Novi Sad, Trg D. Obradovića 6, Novi Sad, 21000, Serbia
  • bDepartment of Mathematics and Informatics, Faculty of Science, University of Novi Sad, Trg D. Obradovića 4, Novi Sad, 21000, Serbia

Abstract

{P1, P2}-Nekrasov matrices represent a generalization of Nekrasov matrices via permutations. In this paper, we obtained an error bound for linear complementarity problems for {P1, P2}-Nekrasov matrices. Numerical examples are given to illustrate that new error bound can give tighter results compared to already known bounds when applied to Nekrasov matrices. Also, we presented new max-norm bounds for the inverse of {P1, P2}-Nekrasov matrices in the block case, considering two different types of block generalizations. Numerical examples show that new norm bounds for the block case can give tighter results compared to already known bounds for the point-wise case. © 2021, University of Nis. All rights reserved.

Author keywords

Linear complementarity problemsMaximum norm boundsNekrasov matricesPermutations

Funding details

Funding sponsor Funding number Acronym
Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja174019MPNTR

  • 1

    2010 Mathematics Subject Classification. 15A18; 15B99 Keywords. Nekrasov matrices, Permutations, Maximum norm bounds, Linear complementarity problems. Received: 15 February 2020; Revised: 15 July 2020; Accepted: 23 October 2020 Communicated by Marko Petković Research partly supported by the Ministry of Education, Science and Technological Development of Serbia, grant 174019. Email addresses: [email protected] (M. Nedović), [email protected] (Lj. Cvetković)

  • ISSN: 03545180
  • Source Type: Journal
  • Original language: English
  • DOI: 10.2298/FIL2101239N
  • Document Type: Article
  • Publisher: University of Nis


© Copyright 2021 Elsevier B.V., All rights reserved.

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