Sabelfeld / Dimov | Monte Carlo Methods and Applications | E-Book | sack.de
E-Book

E-Book, Englisch, 246 Seiten

Reihe: De Gruyter Proceedings in Mathematics

Sabelfeld / Dimov Monte Carlo Methods and Applications


Proceedings of the Eighth IMACS Seminar on Monte Carlo Methods, August 29 – September 2, 2011, Borovets, Bulgaria

E-Book, Englisch, 246 Seiten

Reihe: De Gruyter Proceedings in Mathematics

ISBN: 978-3-11-029358-6
Verlag: De Gruyter
Format: PDF
 Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

This is the proceedings of the "8th IMACS Seminar on Monte Carlo Methods" held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures. The history of the IMACS Seminar on Monte Carlo Methods goes back to April 1997 when the first MCM Seminar was organized in Brussels: 1st IMACS Seminar, 1997, Brussels, Belgium
2nd IMACS Seminar, 1999, Varna, Bulgaria
3rd IMACS Seminar, 2001, Salzburg, Austria
4th IMACS Seminar, 2003, Berlin, Germany
5th IMACS Seminar, 2005, Tallahassee, USA
6th IMACS Seminar, 2007, Reading, UK
7th IMACS Seminar, 2009, Brussels, Belgium
8th IMACS Seminar, 2011, Borovets, Bulgaria
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Zielgruppe

PhD Students, Researchers; Specialists in Different Fields using Monte Carlo Methods, e.g. Financial Mathematics, Transport of Particles, Semiconductor Simulation; Academic Libraries

Autoren/Hrsg.

Sabelfeld, Karl K.
Dimov, Ivan

Fachgebiete

Weitere Infos & Material

1;Preface;5
2;1 Improvement of Multi-population Genetic Algorithms Convergence Time;15
2.1;1.1 Introduction;15
2.2;1.2 Short Overview of MpGA Modifications;16
2.3;1.3 Parameter Identification of S. cerevisiae Fed-Batch Cultivation Using Different Kinds of MpGA;18
2.4;1.4 Analysis and Conclusions;21
3;2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA;25
3.1;2.1 Introduction;25
3.2;2.2 The Metropolis Monte Carlo Method;26
3.3;2.3 Decomposition into Subdomains and the Virtual Topology Using OpenMPI;27
3.4;2.4 Management of Hypersphere Coordinate Migration Between Domains;28
3.4.1;2.4.1 Communication between the CPU and the GPU;29
3.5;2.5 Pseudorandom Number Generation;29
3.6;2.6 Results of Running the Modified Code;29
3.7;2.7 Conclusions;32
4;3 Efficient Implementation of the Heston Model Using GPGPU;35
4.1;3.1 Introduction;35
4.2;3.2 Our GPGPU-Based Algorithm for Option Pricing;37
4.3;3.3 Numerical Results;39
4.4;3.4 Conclusions and Future Work;41
5;4 On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2;43
5.1;4.1 Introduction;43
5.2;4.2 Short Remarks on the Game-Method for Modeling from Crisp Point of View;43
5.3;4.3 On the Game-Method for Modeling with Intuitionistic Fuzzy Estimations;45
5.4;4.4 Main Results;48
5.5;4.5 Conclusion;50
6;5 Generalized Nets, ACO Algorithms, and Genetic Algorithms;53
6.1;5.1 Introduction;53
6.2;5.2 ACO and GA;54
6.3;5.3 GN for Hybrid ACO-GA Algorithm;56
6.4;5.4 Conclusion;58
7;6 Bias Evaluation and Reduction for Sample-Path Optimization;61
7.1;6.1 Introduction;61
7.2;6.2 Problem Formulation;63
7.3;6.3 Taylor-Based Bias Correction;65
7.4;6.4 Impact on the Optimization Bias;66
7.5;6.5 Numerical Experiments;67
7.6;6.6 Conclusions;69
8;7 Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers;73
8.1;7.1 Introduction;73
8.2;7.2 QCL Transport Model;73
8.2.1;7.2.1 Pauli Master Equation;74
8.2.2;7.2.2 Calculation of Basis States;75
8.2.3;7.2.3 Monte Carlo Solver;76
8.3;7.3 Results and Discussion;78
8.4;7.4 Conclusion;79
9;8 Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering;83
9.1;8.1 Introduction;83
9.2;8.2 General Particle Filtering Framework;84
9.3;8.3 High Dimensional Particle Schemes;85
9.3.1;8.3.1 Sequential MCMC Filtering;85
9.3.2;8.3.2 Efficient Sampling in High Dimensions;86
9.3.3;8.3.3 Setting Proposal and Steering Distributions;87
9.4;8.4 Illustrative Examples;87
9.5;8.5 Conclusions;90
10;9 Game-Method for Modeling and WRF-Fire Model Working Together;93
10.1;9.1 Introduction;93
10.2;9.2 Description of the Game-Method for Modeling;94
10.3;9.3 General Description of the Coupled Atmosphere Fire Modeling and WRF-Fire;95
10.4;9.4 Wind Simulation Approach;97
10.5;9.5 Conclusion;98
11;10 Wireless Sensor Network Layout;101
11.1;10.1 Introduction;101
11.2;10.2 Wireless Sensor Network Layout Problem;102
11.3;10.3 ACO for WSN Layout Problem;104
11.4;10.4 Experimental Results;106
11.5;10.5 Conclusion;107
12;11 A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator;111
12.1;11.1 Introduction;111
12.2;11.2 Modeling Oxidation Kinetics;112
12.3;11.3 Development of the Lorentzian Model;114
12.3.1;11.3.1 Algorithm for the Gaussian Model;114
12.3.2;11.3.2 Development of the Lorentzian Model;115
12.4;11.4 Conclusion;117
13;12 Stratified Monte Carlo Integration;119
13.1;12.1 Introduction;119
13.2;12.2 Numerical Integration;120
13.3;12.3 Conclusion;126
14;13 Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation;129
14.1;13.1 Motivation;129
14.2;13.2 AFFFF;130
14.3;13.3 Mathematical Model and Numerical Algorithm;131
14.3.1;13.3.1 Mathematical Model;131
14.3.2;13.3.2 The MLMC Algorithm;132
14.4;13.4 Numerical Results;133
15;14 Convexization in Markov Chain Monte Carlo;139
15.1;14.1 Introduction;139
15.2;14.2 Auxiliary Functions;140
15.2.1;14.2.1 Definition of Auxiliary Functions;140
15.2.2;14.2.2 Optimization Process for Auxiliary Functions;140
15.2.3;14.2.3 Auxiliary Functions for Convex Functions;142
15.2.4;14.2.4 Objective Function Which Is the Sum of Convex and Concave Functions;142
15.3;14.3 Stochastic Auxiliary Functions;143
15.3.1;14.3.1 Stochastic Convex Learning (Summary);143
15.3.2;14.3.2 Auxiliary Stochastic Functions;144
15.4;14.4 Metropolis-Hastings Auxiliary Algorithm;144
15.5;14.5 Numerical Experiments;145
15.6;14.6 Conclusion;146
16;15 Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation;149
16.1;15.1 Introduction;149
16.2;15.2 Value Simulation for Integral Equations;151
16.2.1;15.2.1 Value Simulation of the Time Interval Between Interactions;152
16.2.2;15.2.2 VSIPN to Estimate the Monomer Concentration Jh1;153
16.2.3;15.2.3 VSIPN to Estimate the Monomer and Dimer Concentration Jh12;154
16.3;15.3 Results of the Numerical Experiments;155
16.4;15.4 Conclusion;157
17;16 Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle;159
17.1;16.1 Introduction;159
17.2;16.2 The Simulated Annealing Method;160
17.3;16.3 Successful Algorithms for Solving the Three-Dimensional Puzzle Using MPI;161
17.3.1;16.3.1 An Embarrassingly Parallel Algorithm;162
17.3.2;16.3.2 Distributed Simulated Annealing Using a Master/Worker Organization;163
17.4;16.4 Results;163
17.5;16.5 Conclusions;166
18;17 The Efficiency Study of Splitting and Branching in the Monte Carlo Method;169
18.1;17.1 Introduction;169
18.2;17.2 Randomized Branching;170
18.3;17.3 Splitting;173
19;18 On the Asymptotics of a Lower Bound for the Diaphony of Generalized van der Corput Sequences;177
19.1;18.1 Introduction and Main Result;177
19.2;18.2 Definitions and Previous Results;179
19.3;18.3 Proof of Theorem 18.1;180
20;19 Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterized Likelihood Function;185
20.1;19.1 Motivation;185
20.2;19.2 Group Object Tracking within the Sequential Monte Carlo Framework;186
20.3;19.3 Measurement Likelihood for Group Object Tracking;187
20.3.1;19.3.1 Introduction of the Notion of the Visible Surface;188
20.3.2;19.3.2 Parametrization of the Visible Surface;189
20.4;19.4 Performance Evaluation;189
20.5;19.5 Conclusions;191
21;20 The Template Design Problem: A Perspective with Metaheuristics;195
21.1;20.1 Introduction;195
21.2;20.2 The Template Design Problem;196
21.3;20.3 Solving the TDP under Deterministic Demand;197
21.3.1;20.3.1 Representation and Evaluation;197
21.3.2;20.3.2 Metaheuristic Approaches;199
21.4;20.4 Experimental Results;200
21.5;20.5 Conclusions and Future Work;204
22;21 A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification;207
22.1;21.1 Introduction;207
22.2;21.2 Genetic Algorithm;208
22.3;21.3 Simulated Annealing;209
22.4;21.4 E. coli MC4110 Fed-Batch Cultivation Process Model;210
22.5;21.5 Numerical Results and Discussion;211
22.6;21.6 Conclusion;212
23;22 Monte Carlo Investigations of Electron Decoherence due to Phonons;217
23.1;22.1 Introduction;217
23.2;22.2 The Algorithms;219
23.2.1;22.2.1 Algorithm A;220
23.2.2;22.2.2 Algorithm B;221
23.2.3;22.2.3 Algorithm C;221
24;23 Geometric Allocation Approach for the Transition Kernel of a Markov Chain;227
24.1;23.1 Introduction;227
24.2;23.2 Geometric Approach;228
24.2.1;23.2.1 Reversible Kernel;230
24.2.2;23.2.2 Irreversible Kernel;231
24.3;23.3 Benchmark Test;231
24.4;23.4 Conclusion;233
25;24 Exact Sampling for the Ising Model at All Temperatures;237
25.1;24.1 Introduction;237
25.2;24.2 The Ising Model;238
25.3;24.3 Exact Sampling;241
25.4;24.4 The Random Cluster Model;242
25.5;24.5 Exact Sampling for the Ising Model;244

Karl K. Sabelfeld, Institute of Computational Mathematics and Geophysics, Russian Acacemy of Sciences, Novosibirsk, Russia; Ivan Dimov, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria.

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