- On a class of superlinearly convergent polynomial time interior point methods for sufficient LCP Florian A. Potra(potramath.umbc.edu) Josef Stoer(jstoermathematik.uni-wuerzburg.de) Abstract: A new class of infeasible interior point methods for solving sufficient linear complementarity problems requiring one matrix factorization and $m$ backsolves at each iteration is proposed and analyzed. The algorithms from this class use a large $(\caln_\infty^-$) neighborhood of an infeasible central path associated with the complementarity problem and an initial positive, but not necessarily feasible, starting point. The Q-order of convergence of the complementarity gap, the residual, and the iteration sequence is $m+1$ for problems that admit a strict complementarity solution and $(m+1)/2$ for general sufficient linear complementarity problems. The methods do not depend on the handicap $\kappa$ of the sufficient LCP. If the starting point is feasible (or almost" feasible) the proposed algorithms have ${\cal O}( (1+\kappa)(1+\log\sqrt[m]{1+\kappa}\,) \sqrt{n}\;L)$ iteration complexity, while if the starting point is large enough" the iteration complexity is ${\cal O}((1+\kappa)^{2+1/m}(1+\log\sqrt[m]{1+\kappa}\,){n}\;L )$. Keywords: linear complementarity, interior point, affine scaling, large neighborhood, superlinear convergence Category 1: Complementarity and Variational Inequalities Citation: Technical Report TR2008-2, Department of Mathematics and Statistics, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 22150, USA, February/2008. Download: [PDF]Entry Submitted: 03/03/2008Entry Accepted: 03/03/2008Entry Last Modified: 03/03/2008Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society and by the Optimization Technology Center.