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A SELF-REGULAR NEWTON BASED ALGORITHM FOR LINEAR OPTIMIZATION

Published online by Cambridge University Press:  05 February 2010

M. SALAHI
M. SALAHI*
Affiliation:
Department of Mathematics, University of Guilan, Rasht, Iran (email: salahim@guilan.ac.ir, salahi.maziar@gmail.com)
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Abstract

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In this paper, using the framework of self-regularity, we propose a hybrid adaptive algorithm for the linear optimization problem. If the current iterates are far from a central path, the algorithm employs a self-regular search direction, otherwise the classical Newton search direction is employed. This feature of the algorithm allows us to prove a worst case iteration bound. Our result matches the best iteration bound obtained by the pure self-regular approach and improves on the worst case iteration bound of the classical algorithm.

Keywords

linear optimizationinterior point methodsself-regular functionspolynomial complexity

MSC classification

Secondary: 90C05: Linear programming
Type
Research Article
Information
The ANZIAM Journal , Volume 51 , Issue 2 , October 2009 , pp. 286 - 301
Copyright
Copyright © Australian Mathematical Society 2010

References

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