Corrector-predictor methods for monotone linear complementarity problems in a wide neighborhood of the central path

被引:13
作者
Potra, Florian A. [1 ]
机构
[1] Univ Maryland Baltimore Cty, Dept Math & Stat, Baltimore, MD 21250 USA
关键词
linear complementarity problem; interior-point algorithm; large neighborhood; superlinear convergence;
D O I
10.1007/s10107-006-0068-2
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Two corrector - predictor interior point algorithms are proposed for solving monotone linear complementarity problems. The algorithms produce a sequence of iterates in the N-infinity(-) neighborhood of the central path. The first algorithm uses line search schemes requiring the solution of higher order polynomial equations in one variable, while the line search procedures of the second algorithm can be implemented in O(m n(1+alpha)) arithmetic operations, where n is the dimension of the problems, alpha is an element of (0, 1] is a constant, and m is the maximum order of the predictor and the corrector. If m = Omega(log n) then both algorithms have O(root nL) iteration complexity. They are superlinearly convergent even for degenerate problems.
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页码:243 / 272
页数:30
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