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Title of article :
Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions Original Research Article
Author/Authors :
Y.B. Zhao، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2007
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Abstract :
It is well known that a wide-neighborhood interior-point algorithm for linear programming performs much better in implementation than its small-neighborhood counterparts. In this paper, we provide a unified way to enlarge the neighborhoods of predictor–corrector interior-point algorithms for linear programming. We prove that our methods not only enlarge the neighborhoods but also retain the so-far best known iteration complexity and superlinear (or quadratic) convergence of the original interior-point algorithms. The idea of our methods is to use the global minimizers of proximity measure functions.
Journal title :
Applied Numerical Mathematics
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Link To Document :