Interior-Point Algorithms Based on Primal-Dual Entropy
Abstract
We propose a family of search directions based on primal-dual entropy in the context of interior point methods for linear programming. This new family contains previously proposed search directions in the context of primal-dual entropy. We analyze the new family of search directions by studying their primal-dual affine-scaling and constant-gap centering components. We then design primal-dual interior-point algorithms by utilizing our search directions in a homogeneous and self-dual framework. We present iteration complexity analysis of our algorithms and provide the results of computational experiments on NETLIB problems.
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Cite this version of the work
Shen Luo
(2006).
Interior-Point Algorithms Based on Primal-Dual
Entropy. UWSpace.
http://hdl.handle.net/10012/1181
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