scholarly journals CKV-type $ B $-matrices and error bounds for linear complementarity problems

2021 ◽  
Vol 6 (10) ◽  
pp. 10846-10860
Author(s):  
Xinnian Song ◽  
◽  
Lei Gao
Keyword(s):  
Error Bound ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Type B ◽  
Numerical Examples ◽  
Infinity Norm ◽  
Norm Bound ◽  
Special Case ◽  
Better Than

<abstract><p>In this paper, we introduce a new subclass of $ P $-matrices called Cvetković-Kostić-Varga type $ B $-matrices (CKV-type $ B $-matrices), which contains DZ-type-$ B $-matrices as a special case, and present an infinity norm bound for the inverse of CKV-type $ B $-matrices. Based on this bound, we also give an error bound for linear complementarity problems of CKV-type $ B $-matrices. It is proved that the new error bound is better than that provided by Li et al. <sup>[<xref ref-type="bibr" rid="b24">24</xref>]</sup> for DZ-type-$ B $-matrices, and than that provided by M. García-Esnaola and J.M. Peña <sup>[<xref ref-type="bibr" rid="b10">10</xref>]</sup> for $ B $-matrices in some cases. Numerical examples demonstrate the effectiveness of the obtained results.</p></abstract>

scholarly journals New error bounds for linear complementarity problems of Σ-SDD matrices and SB-matrices

2019 ◽  
Vol 17 (1) ◽  
pp. 1599-1614
Author(s):  
Zhiwu Hou ◽  
Xia Jing ◽  
Lei Gao
Keyword(s):  
Linear Algebra ◽  
Error Bound ◽  
Linear Complementarity Problem ◽  
Error Bounds ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Numerical Examples ◽  
Numer Algor ◽  
Better Than

Abstract A new error bound for the linear complementarity problem (LCP) of Σ-SDD matrices is given, which depends only on the entries of the involved matrices. Numerical examples are given to show that the new bound is better than that provided by García-Esnaola and Peña [Linear Algebra Appl., 2013, 438, 1339–1446] in some cases. Based on the obtained results, we also give an error bound for the LCP of SB-matrices. It is proved that the new bound is sharper than that provided by Dai et al. [Numer. Algor., 2012, 61, 121–139] under certain assumptions.

scholarly journals Norm bounds for the inverse and error bounds for linear complementarity problems for {P1,P2}-Nekrasov matrices

Filomat ◽  
2021 ◽  
Vol 35 (1) ◽  
pp. 239-250
Author(s):  
M. Nedovic ◽  
Lj. Cvetkovic
Keyword(s):  
Error Bound ◽  
Error Bounds ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Numerical Examples ◽  
Different Types ◽  
Nekrasov Matrices

{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 fP1; P2g-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.

scholarly journals The Solution Set Characterization and Error Bound for the Extended Mixed Linear Complementarity Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Hongchun Sun ◽  
Yiju Wang
Keyword(s):  
Error Bound ◽  
Linear Complementarity Problem ◽  
Complementarity Problem ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Solution Set ◽  
Global Error Bound ◽  
Mixed Linear Complementarity Problem

For the extended mixed linear complementarity problem (EML CP), we first present the characterization of the solution set for the EMLCP. Based on this, its global error bound is also established under milder conditions. The results obtained in this paper can be taken as an extension for the classical linear complementarity problems.

scholarly journals An improved convergence based on accelerated modulus-based Gauss–Seidel method for interactive rigid body simulations

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Shugo Miyamoto ◽  
Makoto Yamashita
Keyword(s):  
Rigid Body ◽  
Numerical Experiments ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Matrix Splitting ◽  
Seidel Method ◽  
The Matrix ◽  
Novel Method ◽  
Special Case

AbstractIn this paper, we propose a novel method that fits linear complementarity problems arising in interactive rigid-body simulations, based on the accelerated modulus-based Gauss–Seidel (AMGS) method. We give a new sufficient condition for the convergence of the generated sequence under a milder condition on the matrix splitting than the special case of the AMGS method. This gives a flexibility in the choice of the matrix splitting, and an appropriate matrix splitting can lead to a better convergence rate in practice. Numerical experiments show that the proposed method is more efficient than the simple application of the AMGS method, and that the accuracy in each step of the proposed method is superior to that of the projected Gauss–Seidel method.

scholarly journals Note on error bounds for linear complementarity problems involving $ B^S $-matrices

2021 ◽  
Vol 7 (2) ◽  
pp. 1896-1906
Author(s):  
Deshu Sun ◽  
Keyword(s):  
Error Bounds ◽  
Complementarity Problems ◽  
Matrix Theory ◽  
Linear Complementarity Problems ◽  
Linear Complementarity ◽  
Upper Bounds ◽  
Theory Analysis ◽  
Infinity Norm ◽  
S Matrix ◽  
Diagonally Dominant

<abstract><p>Using the range for the infinity norm of inverse matrix of a strictly diagonally dominant $ M $-matrix, some new error bounds for the linear complementarity problem are obtained when the involved matrix is a $ B^S $-matrix. Theory analysis and numerical examples show that these upper bounds are more accurate than some existing results.</p></abstract>

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