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Product Distributions for Distributed OptimizationWith connections to bounded rational game theory, information theory and statistical mechanics, Product Distribution (PD) theory provides a new framework for performing distributed optimization. Furthermore, PD theory extends and formalizes Collective Intelligence, thus connecting distributed optimization to distributed Reinforcement Learning (FU). This paper provides an overview of PD theory and details an algorithm for performing optimization derived from it. The approach is demonstrated on two unconstrained optimization problems, one with discrete variables and one with continuous variables. To highlight the connections between PD theory and distributed FU, the results are compared with those obtained using distributed reinforcement learning inspired optimization approaches. The inter-relationship of the techniques is discussed.
Document ID
20050082001
Acquisition Source
Headquarters
Document Type
Preprint (Draft being sent to journal)
Authors
Bieniawski, Stefan R.
(Stanford Univ. Stanford, CA, United States)
Wolpert, David H.
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 22, 2013
Publication Date
January 1, 2004
Subject Category
Systems Analysis And Operations Research
Distribution Limits
Public
Copyright
Public Use Permitted.
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