An O(√nL) iteration primal-dual path-following method, based on wide neighborhoods and large updates, for monotone LCP

Wenbao Ai, Shuzhong Zhang

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

Abstract

In this paper we propose a new class of primal-dual path-following interior point algorithms for solving monotone linear complementarity problems. At each iteration, the method would select a target on the central path with a large update from the current iterate, and then the Newton method is used to get the search directions, followed by adaptively choosing the step sizes, which are, e.g., the largest possible steps before leaving a neighborhood that is as wide as the N- neighborhood. The only deviation from the classical approach is that we treat the classical Newton direction as the sum of two other directions, corresponding to, respectively, the negative part and the positive part of the right-hand side. We show that if these two directions are equipped with different and appropriate step sizes, then the method enjoys the low iteration bound of O(√n log L], where n is the dimension of the problem and L = ((x0)T s0/ε with ε the required precision and (x0, s0) the initial interior solution. For a predictor-corrector variant of the method, we further prove that, besides the predictor steps, each corrector step also reduces the duality gap by a rate of 1-1/O(√n). Additionally, if the problem has a strict complementary solution, then the predictor steps converge Q-quadratically.

Original languageEnglish (US)
Pages (from-to)400-417
Number of pages18
JournalSIAM Journal on Optimization
Volume16
Issue number2
DOIs
StatePublished - 2006

Keywords

  • Monotone linear complementarity problem
  • Primal-dual interior point method
  • Wide neighborhood

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