On the Information Content of Some Stochastic Algorithms

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We formulate an optimization stochastic algorithm convergence theorem, of Solis and Wets type, and we show several instances of its application to concrete algorithms. In this convergence theorem the algorithm is a sequence of random variables and, in order to describe the increasing flow of information associated to this sequence we define a filtration – or flow of σ -algebras – on the probability space, depending on the sequence of random variables and on the function being optimized. We compare the flow of information of two convergent algorithms by comparing the associated filtrations by means of the Cotter distance of σ -algebras. The main result is that two convergent optimization algorithms have the same information content if both their limit minimization functions generate the full σ -algebra of the probability space.

Original languageEnglish
Title of host publicationRecent Developments in Stochastic Methods and Applications - ICSM-5, Selected Contributions
EditorsAlbert N. Shiryaev, Konstantin E. Samouylov, Dmitry V. Kozyrev
Place of PublicationCham
Number of pages19
ISBN (Electronic)978-3-030-83266-7
ISBN (Print)978-3-030-83265-0
Publication statusPublished - 2021
Event5th International Conference on Stochastic Methods, ICSM-5 2020 - Moscow, Russian Federation
Duration: 23 Nov 202027 Nov 2020

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Conference5th International Conference on Stochastic Methods, ICSM-5 2020
Country/TerritoryRussian Federation


  • Convergence of information σ -fields
  • Global optimization
  • Stochastic algorithms


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