Title :
Convergence of algorithms for solving the Nearest Point Problem in Reduced Convex Hulls
Author :
López, Jorge ; Dorronsoro, José R.
Author_Institution :
Dept. of Comput. Sci., Univ. Autonoma de Madrid, Madrid, Spain
fDate :
July 31 2011-Aug. 5 2011
Abstract :
In this paper we establish a framework for the convergence of two algorithms for solving the Nearest Point Problem in Reduced Convex Hulls (RCH-NPP), namely the RCH-GSK method proposed in [1] and the RCH-MDM method suggested in [2]. This framework allows us to show the asymptotic convergence of both methods in a very simple way. Moreover, it allows to justify a shrinking strategy for RCH-MDM.
Keywords :
convergence; convex programming; set theory; Gilbert-Schlesinger-Kozinec method; Mitchell-Dem´yanov-Malozemov method; RCH-GSK method; RCH-MDM method; RCH-NPP method; algorithm convergence; asymptotic convergence; nearest point problem; reduced convex hull; Buildings; Cognition; Convergence; Equations; Sorting; Support vector machines; Writing;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033251
