Geodesic
A theorem on the convergence of algorithms of static stochastic optimization
Mathematica Applicanda, Tome 4 (1976) no. 7, pp. 81-99.

Voir la notice de l'article provenant de la source Annales Societatis Mathematicae Polonae Series

The problem of this paper is to minimize a function f, which is scalar-valued and defined on a finite dimensional vector space. An iterative algorithm is of the form X(n+1)=A(n)(X(n)) and can take the usual form X(n+1)=X(n)−a(n)Y(n), where Y(n) can be as in the Kiefer-Wolfowitz procedure, but an is random. Making use of the theorem on convergence of supermartingales the author gives several theorems on the convergence of the procedure to the minimal point of f.
DOI : 10.14708/ma.v4i7.1199
Mots-clés : 93E10 
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J. Koronacki. A theorem on the convergence of algorithms of static stochastic optimization. Mathematica Applicanda, Tome 4 (1976) no. 7, pp.  81-99. doi : 10.14708/ma.v4i7.1199. http://geodesic.mathdoc.fr/articles/10.14708/ma.v4i7.1199/

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