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Some Noninterior Continuation Methods for LinearComplementarity Problems

Published: 01 October 1996 Publication History

Abstract

We introduce some new path-following methods for the solution of the linear complementarity problem. We call these methods noninterior continuation methods since, in contrast to interior-point methods, not all iterates have to stay in the positive orthant. This is possible since we reformulate certain perturbed complementarity problems as a nonlinear system of equations. However, similar to interior-point methods, we also try to follow the central path. We present some conditions which guarantee the existence of this central path, prove a global convergence result for some implementable noninterior continuation methods, and report some numerical results obtained with these methods. We also prove global error bound results for the perturbed linear complementarity problems.

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  • (2022)A new class of neural networks for NCPs using smooth perturbations of the natural residual functionJournal of Computational and Applied Mathematics10.1016/j.cam.2022.114092407:COnline publication date: 1-Jun-2022
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Published In

cover image SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications  Volume 17, Issue 4
Oct. 1996
336 pages
ISSN:0895-4798
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Society for Industrial and Applied Mathematics

United States

Publication History

Published: 01 October 1996

Author Tags

  1. $ P_0$-matrix
  2. $ R_0$-matrix
  3. global error bounds
  4. interior-point methods
  5. linear complementarity problems
  6. path-following methods

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  • (2022)A new class of neural networks for NCPs using smooth perturbations of the natural residual functionJournal of Computational and Applied Mathematics10.1016/j.cam.2022.114092407:COnline publication date: 1-Jun-2022
  • (2022)An adaptive mesh refinement method for indirectly solving optimal control problemsNumerical Algorithms10.1007/s11075-022-01259-191:1(193-225)Online publication date: 1-Sep-2022
  • (2022)An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex ProgrammingJournal of Scientific Computing10.1007/s10915-022-01765-390:3Online publication date: 1-Mar-2022
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  • (2019)A sub-additive DC approach to the complementarity problemComputational Optimization and Applications10.1007/s10589-019-00078-w73:2(509-534)Online publication date: 1-Jun-2019
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