Corrector-Predictor Methods for Sufficient Linear Complementarity Problems in a Wide Neighborhood of the Central Path

Authors:
Xing Liu;Florian A. Potra
Affiliations:
-;-
Venue:
SIAM Journal on Optimization
Year:
2006

Citing 0
Cited 5

On a Class of Superlinearly Convergent Polynomial Time Interior Point Methods for Sufficient LCP

SIAM Journal on Optimization
Corrector-predictor methods for sufficient linear complementarity problems

Computational Optimization and Applications
Unified Analysis of Kernel-Based Interior-Point Methods for $P_*(\kappa)$-Linear Complementarity Problems

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Nondegenerate piecewise linear systems: A finite newton algorithm and applications in machine learning

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A full-Newton step feasible interior-point algorithm for P*(κ)-linear complementarity problems

Journal of Global Optimization

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Abstract

A higher order corrector-predictor interior-point method is proposed for solving sufficient linear complementarity problems. The algorithm produces a sequence of iterates in the $\caln_{\infty}^{-}$ neighborhood of the central path. The algorithm does not depend on the handicap &kgr; of the problem. It has $O((1+\kappa)\sqrt{n}L)$ iteration complexity and is superlinearly convergent even for degenerate problems.