Paper Title:
Aggregate Homotopy Method for Min-Max-Min Programming Satisfying a Weak-Normal Cone Condition
  Abstract

Min-max-min programming is an important but difficult nonsmooth programming. An aggregate homotopy method was given for solving min-max-min programming by Bo Yu el al. However, the method requires a difficult to verify weak-normal cone condition. Moreover, the method is only theoretically with no algorithmic implementation. In this paper, the weak normal cone condition is discussed first. A class of min-max-min programming satisfying the condition is introduced. A detailed algorithm to implement the method is presented. Models arising from some applications such as support vector machine for multiple-instance classification in data mining, can be included in the problem. In the end, the aggregate homotopy method is given to solve the multiple-instance support vector machine model.

  Info
Periodical
Edited by
Shaobo Zhong, Yimin Cheng and Xilong Qu
Pages
669-672
DOI
10.4028/www.scientific.net/AMM.50-51.669
Citation
H. J. Xiong, B. Yu, "Aggregate Homotopy Method for Min-Max-Min Programming Satisfying a Weak-Normal Cone Condition", Applied Mechanics and Materials, Vols. 50-51, pp. 669-672, 2011
Online since
February 2011
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$32.00
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