9th Joint International Conference on Information Sciences (JCIS-06)

Optimal Quantization : Evolutionary Algorithm vs Stochastic Gradient

Authors
Moez MRAD 0, Sana BEN HAMIDA
Corresponding Author
Moez MRAD
0Societe Generale and CERMSEM, Universite Paris I , France
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.161How to use a DOI?
Keywords
Evolutionary Optimization , Stochastic Gradient, Quantization
Abstract
We propose a new method based on evolutionary optimization for obtaining an optimal Lp-quantizer of a multidimensional random variable. First, we remind briefly the main results about quantization. Then, we present the classical gradient-based approach used up to now to find a “local” optimal Lp-quantizer. Then, we give an algorithm that permits to deal with the problem in the evolutionary optimization framework and illustrate a numerical comparison between the proposed method and the stochastic gradient method. Finally, a numerical application to option pricing in finance is provided.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.161How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Moez MRAD
AU  - Sana BEN HAMIDA
PY  - 2006/10
DA  - 2006/10
TI  - Optimal Quantization : Evolutionary Algorithm vs Stochastic Gradient
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.161
DO  - https://doi.org/10.2991/jcis.2006.161
ID  - MRAD2006/10
ER  -