Intro -- Preface -- 1 Improvement of Multi-population Genetic Algorithms Convergence Time -- 1.1 Introduction -- 1.2 Short Overview of MpGA Modifications -- 1.3 Parameter Identification of S. cerevisiae Fed-Batch Cultivation Using Different Kinds of MpGA -- 1.4 Analysis and Conclusions -- 2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA -- 2.1 Introduction -- 2.2 The Metropolis Monte Carlo Method -- 2.3 Decomposition into Subdomains and the Virtual Topology Using OpenMPI -- 2.4 Management of Hypersphere Coordinate Migration Between Domains -- 2.4.1 Communication between the CPU and the GPU -- 2.5 Pseudorandom Number Generation -- 2.6 Results of Running the Modified Code -- 2.7 Conclusions -- 3 Efficient Implementation of the Heston Model Using GPGPU -- 3.1 Introduction -- 3.2 Our GPGPU-Based Algorithm for Option Pricing -- 3.3 Numerical Results -- 3.4 Conclusions and Future Work -- 4 On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2 -- 4.1 Introduction -- 4.2 Short Remarks on the Game-Method for Modeling from Crisp Point of View -- 4.3 On the Game-Method for Modeling with Intuitionistic Fuzzy Estimations -- 4.4 Main Results -- 4.5 Conclusion -- 5 Generalized Nets, ACO Algorithms, and Genetic Algorithms -- 5.1 Introduction -- 5.2 ACO and GA -- 5.3 GN for Hybrid ACO-GA Algorithm -- 5.4 Conclusion -- 6 Bias Evaluation and Reduction for Sample-Path Optimization -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.3 Taylor-Based Bias Correction -- 6.4 Impact on the Optimization Bias -- 6.5 Numerical Experiments -- 6.6 Conclusions -- 7 Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers -- 7.1 Introduction -- 7.2 QCL Transport Model -- 7.2.1 Pauli Master Equation -- 7.2.2 Calculation of Basis States -- 7.2.3 Monte Carlo Solver.

7.3 Results and Discussion -- 7.4 Conclusion -- 8 Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering -- 8.1 Introduction -- 8.2 General Particle Filtering Framework -- 8.3 High Dimensional Particle Schemes -- 8.3.1 Sequential MCMC Filtering -- 8.3.2 Efficient Sampling in High Dimensions -- 8.3.3 Setting Proposal and Steering Distributions -- 8.4 Illustrative Examples -- 8.5 Conclusions -- 9 Game-Method for Modeling and WRF-Fire Model Working Together -- 9.1 Introduction -- 9.2 Description of the Game-Method for Modeling -- 9.3 General Description of the Coupled Atmosphere Fire Modeling and WRF-Fire -- 9.4 Wind Simulation Approach -- 9.5 Conclusion -- 10 Wireless Sensor Network Layout -- 10.1 Introduction -- 10.2 Wireless Sensor Network Layout Problem -- 10.3 ACO for WSN Layout Problem -- 10.4 Experimental Results -- 10.5 Conclusion -- 11 A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator -- 11.1 Introduction -- 11.2 Modeling Oxidation Kinetics -- 11.3 Development of the Lorentzian Model -- 11.3.1 Algorithm for the Gaussian Model -- 11.3.2 Development of the Lorentzian Model -- 11.4 Conclusion -- 12 Stratified Monte Carlo Integration -- 12.1 Introduction -- 12.2 Numerical Integration -- 12.3 Conclusion -- 13 Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation -- 13.1 Motivation -- 13.2 AFFFF -- 13.3 Mathematical Model and Numerical Algorithm -- 13.3.1 Mathematical Model -- 13.3.2 The MLMC Algorithm -- 13.4 Numerical Results -- 14 Convexization in Markov Chain Monte Carlo -- 14.1 Introduction -- 14.2 Auxiliary Functions -- 14.2.1 Definition of Auxiliary Functions -- 14.2.2 Optimization Process for Auxiliary Functions -- 14.2.3 Auxiliary Functions for Convex Functions -- 14.2.4 Objective Function Which Is the Sum of Convex and Concave Functions.

14.3 Stochastic Auxiliary Functions -- 14.3.1 Stochastic Convex Learning (Summary) -- 14.3.2 Auxiliary Stochastic Functions -- 14.4 Metropolis-Hastings Auxiliary Algorithm -- 14.5 Numerical Experiments -- 14.6 Conclusion -- 15 Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation -- 15.1 Introduction -- 15.2 Value Simulation for Integral Equations -- 15.2.1 Value Simulation of the Time Interval Between Interactions -- 15.2.2 VSIPN to Estimate the Monomer Concentration Jh1 -- 15.2.3 VSIPN to Estimate the Monomer and Dimer Concentration Jh12 -- 15.3 Results of the Numerical Experiments -- 15.4 Conclusion -- 16 Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle -- 16.1 Introduction -- 16.2 The Simulated Annealing Method -- 16.3 Successful Algorithms for Solving the Three-Dimensional Puzzle Using MPI -- 16.3.1 An Embarrassingly Parallel Algorithm -- 16.3.2 Distributed Simulated Annealing Using a Master/Worker Organization -- 16.4 Results -- 16.5 Conclusions -- 17 The Efficiency Study of Splitting and Branching in the Monte Carlo Method -- 17.1 Introduction -- 17.2 Randomized Branching -- 17.3 Splitting -- 18 On the Asymptotics of a Lower Bound for the Diaphony of Generalized van der Corput Sequences -- 18.1 Introduction and Main Result -- 18.2 Definitions and Previous Results -- 18.3 Proof of Theorem 18.1 -- 19 Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterized Likelihood Function -- 19.1 Motivation -- 19.2 Group Object Tracking within the Sequential Monte Carlo Framework -- 19.3 Measurement Likelihood for Group Object Tracking -- 19.3.1 Introduction of the Notion of the Visible Surface -- 19.3.2 Parametrization of the Visible Surface -- 19.4 Performance Evaluation -- 19.5 Conclusions.

20 The Template Design Problem: A Perspective with Metaheuristics -- 20.1 Introduction -- 20.2 The Template Design Problem -- 20.3 Solving the TDP under Deterministic Demand -- 20.3.1 Representation and Evaluation -- 20.3.2 Metaheuristic Approaches -- 20.4 Experimental Results -- 20.5 Conclusions and Future Work -- 21 A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification -- 21.1 Introduction -- 21.2 Genetic Algorithm -- 21.3 Simulated Annealing -- 21.4 E. coli MC4110 Fed-Batch Cultivation Process Model -- 21.5 Numerical Results and Discussion -- 21.6 Conclusion -- 22 Monte Carlo Investigations of Electron Decoherence due to Phonons -- 22.1 Introduction -- 22.2 The Algorithms -- 22.2.1 Algorithm A -- 22.2.2 Algorithm B -- 22.2.3 Algorithm C -- 23 Geometric Allocation Approach for the Transition Kernel of a Markov Chain -- 23.1 Introduction -- 23.2 Geometric Approach -- 23.2.1 Reversible Kernel -- 23.2.2 Irreversible Kernel -- 23.3 Benchmark Test -- 23.4 Conclusion -- 24 Exact Sampling for the Ising Model at All Temperatures -- 24.1 Introduction -- 24.2 The Ising Model -- 24.3 Exact Sampling -- 24.4 The Random Cluster Model -- 24.5 Exact Sampling for the Ising Model.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.