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Generalized Stochastic Gradient Learning

George Evans (), Seppo Honkapohja () and Noah Williams ()

No 317, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.

JEL-codes: C62 C65 D83 E10 E17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-evo and nep-mac
Date: 2005-10
Note: TWP
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Related works:
Journal Article: GENERALIZED STOCHASTIC GRADIENT LEARNING (2010) Downloads
Working Paper: Generalized Stochastic Gradient Learning (2008) Downloads
Working Paper: Generalized Stochastic Gradient Learning (2005) Downloads
Working Paper: Generalized Stochastic Gradient Learning (2005) Downloads
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