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Introduction : Main Models and LLN Methodology. / Kolokoltsov, Vassili N.; Malafeyev, Oleg A.

Springer Series in Operations Research and Financial Engineering. Springer Nature, 2019. стр. 1-24 (Springer Series in Operations Research and Financial Engineering).

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Harvard

Kolokoltsov, VN & Malafeyev, OA 2019, Introduction: Main Models and LLN Methodology. в Springer Series in Operations Research and Financial Engineering. Springer Series in Operations Research and Financial Engineering, Springer Nature, стр. 1-24. https://doi.org/10.1007/978-3-030-12371-0_1

APA

Kolokoltsov, V. N., & Malafeyev, O. A. (2019). Introduction: Main Models and LLN Methodology. в Springer Series in Operations Research and Financial Engineering (стр. 1-24). (Springer Series in Operations Research and Financial Engineering). Springer Nature. https://doi.org/10.1007/978-3-030-12371-0_1

Vancouver

Kolokoltsov VN, Malafeyev OA. Introduction: Main Models and LLN Methodology. в Springer Series in Operations Research and Financial Engineering. Springer Nature. 2019. стр. 1-24. (Springer Series in Operations Research and Financial Engineering). https://doi.org/10.1007/978-3-030-12371-0_1

Author

Kolokoltsov, Vassili N. ; Malafeyev, Oleg A. / Introduction : Main Models and LLN Methodology. Springer Series in Operations Research and Financial Engineering. Springer Nature, 2019. стр. 1-24 (Springer Series in Operations Research and Financial Engineering).

BibTeX

@inbook{31c7c99d3fb248619dcfabc8f85aaac8,
title = "Introduction: Main Models and LLN Methodology",
abstract = "The first sections of this chapter are devoted to a brief introduction to our basic model of controlled mean-field interacting particle systems, its principal features in the framework of pressure and resistance, and to the fundamental paradigm of the scaling limit (dynamic law of large numbers), allowing one to reduce the analysis of these Markov chains with exponentially large state spaces to a low-dimensional dynamic system expressed in terms of simple ordinary differential equations (ODEs), so called kinetic equations.",
author = "Kolokoltsov, {Vassili N.} and Malafeyev, {Oleg A.}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2019",
doi = "10.1007/978-3-030-12371-0_1",
language = "English",
series = "Springer Series in Operations Research and Financial Engineering",
publisher = "Springer Nature",
pages = "1--24",
booktitle = "Springer Series in Operations Research and Financial Engineering",
address = "Germany",

}

RIS

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T1 - Introduction

T2 - Main Models and LLN Methodology

AU - Kolokoltsov, Vassili N.

AU - Malafeyev, Oleg A.

N1 - Publisher Copyright: © 2019, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019

Y1 - 2019

N2 - The first sections of this chapter are devoted to a brief introduction to our basic model of controlled mean-field interacting particle systems, its principal features in the framework of pressure and resistance, and to the fundamental paradigm of the scaling limit (dynamic law of large numbers), allowing one to reduce the analysis of these Markov chains with exponentially large state spaces to a low-dimensional dynamic system expressed in terms of simple ordinary differential equations (ODEs), so called kinetic equations.

AB - The first sections of this chapter are devoted to a brief introduction to our basic model of controlled mean-field interacting particle systems, its principal features in the framework of pressure and resistance, and to the fundamental paradigm of the scaling limit (dynamic law of large numbers), allowing one to reduce the analysis of these Markov chains with exponentially large state spaces to a low-dimensional dynamic system expressed in terms of simple ordinary differential equations (ODEs), so called kinetic equations.

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DO - 10.1007/978-3-030-12371-0_1

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T3 - Springer Series in Operations Research and Financial Engineering

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EP - 24

BT - Springer Series in Operations Research and Financial Engineering

PB - Springer Nature

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ID: 72678932