Empirical Analysis of Stochastic Methods of Linear Algebra

Autor:	Anton Lebedev, Vassil Alexandrov, Mustafa Emre Şahin
Rok vydání:	2020
Předmět:	010104 statistics & probability Stochastic gradient descent Iterative method Computer science Linear algebra Linear system MathematicsofComputing_NUMERICALANALYSIS Applied mathematics 010103 numerical & computational mathematics 0101 mathematics 01 natural sciences Projection (linear algebra)
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030504359 ICCS (7)
Popis:	In this paper we present the results of an empirical study of stochastic projection and stochastic gradient descent methods as means of obtaining approximate inverses and preconditioners for iterative methods. Results of numerical experiments are used to analyse scalability and overall suitability of the selected methods as practical tools for treatment of large linear systems of equations. The results are preliminary due to the code being not yet fully optimized.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0a90f2d0917244a255342ffa811f4918 https://doi.org/10.1007/978-3-030-50436-6_40 Zobrazit plný text záznamu