Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF01585757
Title: On the choice of parameters for power-series interior point algorithms in linear programming
Authors: Zhao, G. 
Keywords: Best parameter
Higher-order derivatives
k-parameter
Linear programming
Power-series interior point algorithms: Parameter transformations
Truncated power-series approximation
Issue Date: Jan-1995
Citation: Zhao, G. (1995-01). On the choice of parameters for power-series interior point algorithms in linear programming. Mathematical Programming 68 (1-3) : 49-71. ScholarBank@NUS Repository. https://doi.org/10.1007/BF01585757
Abstract: In this paper we study higher-order interior point algorithms, especially power-series algorithms, for solving linear programming problems. Since higher-order differentials are not parameter-invariant, it is important to choose a suitable parameter for a power-series algorithm. We propose a parameter transformation to obtain a good choice of parameter, called a k-parameter, for general truncated powerseries approximations. We give a method to find a k-parameter. This method is applied to two powerseries interior point algorithms, which are built on a primal-dual algorithm and a dual algorithm, respectively. Computational results indicate that these higher-order power-series algorithms accelerate convergence compared to first-order algorithms by reducing the number of iterations. Also they demonstrate the efficiency of the k-parameter transformation to amend an unsuitable parameter in power-series algorithms. © 1995 The Mathematical Programming Society, Inc.
Source Title: Mathematical Programming
URI: http://scholarbank.nus.edu.sg/handle/10635/103774
ISSN: 00255610
DOI: 10.1007/BF01585757
Appears in Collections:Staff Publications

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