DocumentCode :
2774457
Title :
The convergence rate of the MDM algorithm
Author :
López, Jorge ; Dorronsoro, José R.
Author_Institution :
Dept. of Comput. Sci., Univ. Autonoma de Madrid, Madrid, Spain
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
In this paper we will describe a simple proof of a linear convergence rate for the MDM algorithm that solves the Minimum Norm Problem (MNP). Linear convergence rates have been shown for the SMO algorithm, but the proofs require specific assumptions and are rather involved. We will follow a different approach, with a more geometric flavor. While as of now our proof also requires some of the just mentioned assumptions, we shall discuss some examples where linear convergence holds without them, suggesting that a linear convergence rate may be achieved under conditions more general than those currently known.
Keywords :
convergence of numerical methods; minimisation; theorem proving; MDM algorithm; MNP; Mitchell-Demyanov and Malozemov algorithm; SMO algorithm; linear convergence rate; minimisation; minimum norm problem; Algorithm design and analysis; Convergence; Face; Minimization; Support vector machines; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252641
Filename :
6252641
Link To Document :
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