Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters

被引：0

作者：

Mikhailov, G. A. ^{[1
,2
]}

机构：

[1] Russian Acad Sci, Inst Computat Math & Math Geophys, Siberian Branch, Novosibirsk 630090, Russia

[2] Novosibirsk State Univ, Novosibirsk 630090, Russia

来源：

DOKLADY MATHEMATICS | 2018年 / 98卷 / 02期

基金：

俄罗斯基础研究基金会;

关键词：

PROBABILITY DENSITY; RADIATIVE-TRANSFER; MODELS;

D O I：

10.1134/S1064562418060157

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Randomized Monte Carlo algorithms intended for statistical kernel estimation of the averaged solution to a problem with random baseline parameters are optimized. For this purpose, a criterion for the complexity of a functional Monte Carlo estimate is formulated. The algorithms involve a splitting method in which, for each realization of the parameters, a certain number of trajectories of the corresponding baseline process are constructed.

引用

页码：448 / 451

页数：4

共 50 条

[1] Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters
G. A. Mikhailov
Doklady Mathematics, 2018, 98 : 448 - 451
[2] Randomized Monte Carlo Algorithms for Problems with Random Parameters (“Double Randomization” Method)
G. A. Mikhailov
Numerical Analysis and Applications, 2019, 12 : 155 - 165
[3] Randomized Monte Carlo Algorithms for Problems with Random Parameters ("Double Randomization" Method)
Mikhailov, G. A.
NUMERICAL ANALYSIS AND APPLICATIONS, 2019, 12 (02) : 155 - 165
[4] Solving boundary value problems with complex parameters by the Monte Carlo method
Mikhailov, GA
Menshchikov, BV
SIBERIAN MATHEMATICAL JOURNAL, 1996, 37 (04) : 775 - 781
[5] POSSIBILITIES OF USING THE MONTE CARLO METHOD FOR SOLVING MACHINING OPTIMIZATION PROBLEMS
Madic, Milos
Radovanovic, Miroslav
FACTA UNIVERSITATIS-SERIES MECHANICAL ENGINEERING, 2014, 12 (01) : 27 - 36
[6] Randomized Monte Carlo algorithms for matrix iterations and solving large systems of linear equations
Sabelfeld, Karl K.
MONTE CARLO METHODS AND APPLICATIONS, 2022, 28 (02): : 125 - 133
[7] OPTIMIZATION IN SOLVING INVERSE PROBLEMS OF ATMOSPHERIC OPTICS BY THE MONTE-CARLO METHOD
ANTYUFEEV, VS
RUSSIAN JOURNAL OF NUMERICAL ANALYSIS AND MATHEMATICAL MODELLING, 1994, 9 (06) : 489 - 514
[8] COMPUTATIONAL ALGORITHMS BASED ON RANDOM SEARCH FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS
MOHAN, C
SHANKER, K
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS, 1990, 33 (1-2) : 115 - 126
[9] Monte Carlo Algorithms for Default Timing Problems
Giesecke, Kay
Kim, Baeho
Zhu, Shilin
MANAGEMENT SCIENCE, 2011, 57 (12) : 2115 - 2129
[10] GFS Algorithm Based on Batch Monte Carlo Trials for Solving Global Optimization Problems
Popkov, Yuri S.
Darkhovskiy, Boris S.
Popkov, Alexey Y.
NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA-2016), 2016, 1776

← 1 2 3 4 5 →