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Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors
| Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 519.3 |
| Collana | Modeling and Simulation in Science, Engineering and Technology |
| Soggetto topico |
Mathematical optimization
Mathematical optimization - Computer programs Models matemàtics Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-93302-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Variability and Heterogeneity in Natural Swarms: Experiments and Modeling -- 1 Introduction -- 2 Sources of Variability in Nature -- 2.1 Development as a Source of Variation -- 2.2 Transient Changes in the Behavior of Individuals -- 2.3 Environmentally Induced Variations -- 2.4 Social Structure -- 2.5 Inherent/Intrinsic Properties and Animal Personality -- 2.6 Variability in Microorganisms -- 3 Experiments with Heterogeneous Swarms -- 3.1 Fish -- 3.2 Mammals -- 3.3 Insects -- 3.4 Microorganisms -- 4 Modeling Heterogeneous Collective Motion -- 4.1 Continuous Models -- 4.2 Agent-Based Models -- 4.3 Specific Examples: Locust -- 4.4 Specific Examples: Microorganisms and Cells -- 5 Summary and Concluding Remarks -- References -- Active Crowds -- 1 Introduction -- 2 Models for Active Particles -- 2.1 Continuous Random Walks -- 2.1.1 Excluded-Volume Interactions -- 2.2 Discrete Random Walks -- 2.3 Hybrid Random Walks -- 3 Models for Externally Activated Particles -- 3.1 Continuous Models -- 3.2 Discrete Models -- 4 General Model Structure -- 4.1 Wasserstein Gradient Flows -- 4.2 Entropy Dissipation -- 5 Boundary Effects -- 5.1 Mass Conserving Boundary Conditions -- 5.2 Flux Boundary Conditions -- 5.3 Other Boundary Conditions -- 6 Active Crowds in the Life and Social Science -- 6.1 Pedestrian Dynamics -- 6.2 Transport in Biological Systems -- 7 Numerical Simulations -- 7.1 One Spatial Dimension -- 7.2 Two Spatial Dimensions -- References -- Mathematical Modeling of Cell Collective Motion Triggered by Self-Generated Gradients -- 1 Introduction -- 2 The Keller-Segel Model and Variations -- 2.1 The Construction of Waves by Keller and Segel -- 2.2 Positivity and Stability Issues -- 2.3 Variations on the Keller-Segel Model -- 2.4 Beyond the Keller-Segel Model: Two Scenarios for SGG.
3 Scenario 1: Strongest Advection at the Back -- 4 Scenario 2: Cell Leakage Compensated by Growth -- 5 Conclusion and Perspectives -- References -- Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker-Planck Interpolation -- 1 Introduction -- 1.1 Mean Shift-Based Methods -- 1.1.1 Lifting the Dynamics to the Wasserstein Space -- 1.2 Spectral Methods -- 1.2.1 Normalized Versions of the Graph Laplacian -- 1.2.2 More General Spectral Embeddings -- 1.3 Outline -- 2 Mean Shift and Fokker-Planck Dynamics on Graphs -- 2.1 Dynamic Interpretation of Spectral Embeddings -- 2.2 The Mean Shift Algorithm on Graphs -- 2.2.1 Mean Shift on Graphs as Inspired by Wasserstein Gradient Flows -- 2.2.2 Quickshift and KNF -- 3 Fokker-Planck Equations on Graphs -- 3.1 Fokker-Planck Equations on Graphs via Interpolation -- 3.2 Fokker-Planck Equation on Graphs via Reweighing and Connections to Graph Mean Shift -- 4 Continuum Limits of Fokker-Planck Equations on Graphs and Implications -- 4.1 Continuum Limit of Mean Shift Dynamics on Graphs -- 4.2 Continuum Limits of Fokker-Planck Equations on Graphs -- 4.3 The Witten Laplacian and Some Implications for Data Clustering -- 5 Numerical Examples -- 5.1 Numerical Method -- 5.2 Simulations -- 5.2.1 Graph Dynamics as Density Dynamics -- 5.2.2 Comparison of Graph Dynamics and PDE Dynamics -- 5.2.3 Clustering Dynamics -- 5.2.4 Effect of the Kernel Density Estimate on Clustering -- 5.2.5 Effect of Data Distribution on Clustering -- 5.2.6 Blue Sky Problem -- 5.2.7 Density vs. Geometry -- References -- Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings -- 1 Introduction -- 2 The Random Batch Methods -- 2.1 The RBM Algorithms -- 2.2 Convergence Analysis -- 2.3 An Illustrating Example: Wealth Evolution -- 3 The Mean-Field Limit -- 4 Molecular Dynamics. 4.1 RBM with Kernel Splitting -- 4.2 Random Batch Ewald: An Importance Sampling in the Fourier Space -- 5 Statistical Sampling -- 5.1 Random Batch Monte Carlo for Many-Body Systems -- 5.2 RBM-SVGD: A Stochastic Version of Stein Variational Gradient Descent -- 6 Agent-Based Models for Collective Dynamics -- 6.1 The Cucker-Smale Model -- 6.2 Consensus Models -- 7 Quantum Dynamics -- 7.1 A Theoretical Result on the N-Body Schrödinger Equation -- 7.1.1 Mathematical Setting and Main Result -- 7.2 Quantum Monte Carlo Methods -- 7.2.1 The Random Batch Method for VMC -- 7.2.2 The Random Batch Method for DMC -- References -- Trends in Consensus-Based Optimization -- 1 Introduction -- 1.1 Notation and Assumptions -- 1.1.1 The Weighted Average -- 2 Consensus-Based Global Optimization Methods -- 2.1 Original Statement of the Method -- 2.1.1 Particle Scheme -- 2.1.2 Mean-Field Limit -- 2.1.3 Analytical Results for the Original Scheme Without Heaviside Function -- 2.1.4 Numerical Methods -- 2.2 Variant 1: Component-Wise Diffusion and Random Batches -- 2.2.1 Component-Wise Geometric Brownian Motion -- 2.2.2 Random Batch Method -- 2.2.3 Implementation and Numerical Results -- 2.3 Variant 2: Component-Wise Common Diffusion -- 2.3.1 Analytical Results -- 2.3.2 Numerical Results -- 3 Relationship of CBO and Particle Swarm Optimization -- 3.1 Variant 4: Personal Best Information -- 3.1.1 Performance -- 4 CBO with State Constraints -- 4.1 Variant 5: Dynamics Constrained to Hyper-Surfaces -- 4.1.1 Analytical Results -- 5 Overview of Applications -- 5.1 Global Optimization Problems: Comparison to Heuristic Methods -- 5.2 Machine Learning -- 5.3 Global Optimization with Constrained State Space -- 5.4 PDE Versus SDE Simulations -- 6 Conclusion, Outlook and Open problems -- References. |
| Record Nr. | UNISA-996466417303316 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors
| Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 519.3 |
| Collana | Modeling and Simulation in Science, Engineering and Technology |
| Soggetto topico |
Mathematical optimization
Mathematical optimization - Computer programs Models matemàtics Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-93302-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Variability and Heterogeneity in Natural Swarms: Experiments and Modeling -- 1 Introduction -- 2 Sources of Variability in Nature -- 2.1 Development as a Source of Variation -- 2.2 Transient Changes in the Behavior of Individuals -- 2.3 Environmentally Induced Variations -- 2.4 Social Structure -- 2.5 Inherent/Intrinsic Properties and Animal Personality -- 2.6 Variability in Microorganisms -- 3 Experiments with Heterogeneous Swarms -- 3.1 Fish -- 3.2 Mammals -- 3.3 Insects -- 3.4 Microorganisms -- 4 Modeling Heterogeneous Collective Motion -- 4.1 Continuous Models -- 4.2 Agent-Based Models -- 4.3 Specific Examples: Locust -- 4.4 Specific Examples: Microorganisms and Cells -- 5 Summary and Concluding Remarks -- References -- Active Crowds -- 1 Introduction -- 2 Models for Active Particles -- 2.1 Continuous Random Walks -- 2.1.1 Excluded-Volume Interactions -- 2.2 Discrete Random Walks -- 2.3 Hybrid Random Walks -- 3 Models for Externally Activated Particles -- 3.1 Continuous Models -- 3.2 Discrete Models -- 4 General Model Structure -- 4.1 Wasserstein Gradient Flows -- 4.2 Entropy Dissipation -- 5 Boundary Effects -- 5.1 Mass Conserving Boundary Conditions -- 5.2 Flux Boundary Conditions -- 5.3 Other Boundary Conditions -- 6 Active Crowds in the Life and Social Science -- 6.1 Pedestrian Dynamics -- 6.2 Transport in Biological Systems -- 7 Numerical Simulations -- 7.1 One Spatial Dimension -- 7.2 Two Spatial Dimensions -- References -- Mathematical Modeling of Cell Collective Motion Triggered by Self-Generated Gradients -- 1 Introduction -- 2 The Keller-Segel Model and Variations -- 2.1 The Construction of Waves by Keller and Segel -- 2.2 Positivity and Stability Issues -- 2.3 Variations on the Keller-Segel Model -- 2.4 Beyond the Keller-Segel Model: Two Scenarios for SGG.
3 Scenario 1: Strongest Advection at the Back -- 4 Scenario 2: Cell Leakage Compensated by Growth -- 5 Conclusion and Perspectives -- References -- Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker-Planck Interpolation -- 1 Introduction -- 1.1 Mean Shift-Based Methods -- 1.1.1 Lifting the Dynamics to the Wasserstein Space -- 1.2 Spectral Methods -- 1.2.1 Normalized Versions of the Graph Laplacian -- 1.2.2 More General Spectral Embeddings -- 1.3 Outline -- 2 Mean Shift and Fokker-Planck Dynamics on Graphs -- 2.1 Dynamic Interpretation of Spectral Embeddings -- 2.2 The Mean Shift Algorithm on Graphs -- 2.2.1 Mean Shift on Graphs as Inspired by Wasserstein Gradient Flows -- 2.2.2 Quickshift and KNF -- 3 Fokker-Planck Equations on Graphs -- 3.1 Fokker-Planck Equations on Graphs via Interpolation -- 3.2 Fokker-Planck Equation on Graphs via Reweighing and Connections to Graph Mean Shift -- 4 Continuum Limits of Fokker-Planck Equations on Graphs and Implications -- 4.1 Continuum Limit of Mean Shift Dynamics on Graphs -- 4.2 Continuum Limits of Fokker-Planck Equations on Graphs -- 4.3 The Witten Laplacian and Some Implications for Data Clustering -- 5 Numerical Examples -- 5.1 Numerical Method -- 5.2 Simulations -- 5.2.1 Graph Dynamics as Density Dynamics -- 5.2.2 Comparison of Graph Dynamics and PDE Dynamics -- 5.2.3 Clustering Dynamics -- 5.2.4 Effect of the Kernel Density Estimate on Clustering -- 5.2.5 Effect of Data Distribution on Clustering -- 5.2.6 Blue Sky Problem -- 5.2.7 Density vs. Geometry -- References -- Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings -- 1 Introduction -- 2 The Random Batch Methods -- 2.1 The RBM Algorithms -- 2.2 Convergence Analysis -- 2.3 An Illustrating Example: Wealth Evolution -- 3 The Mean-Field Limit -- 4 Molecular Dynamics. 4.1 RBM with Kernel Splitting -- 4.2 Random Batch Ewald: An Importance Sampling in the Fourier Space -- 5 Statistical Sampling -- 5.1 Random Batch Monte Carlo for Many-Body Systems -- 5.2 RBM-SVGD: A Stochastic Version of Stein Variational Gradient Descent -- 6 Agent-Based Models for Collective Dynamics -- 6.1 The Cucker-Smale Model -- 6.2 Consensus Models -- 7 Quantum Dynamics -- 7.1 A Theoretical Result on the N-Body Schrödinger Equation -- 7.1.1 Mathematical Setting and Main Result -- 7.2 Quantum Monte Carlo Methods -- 7.2.1 The Random Batch Method for VMC -- 7.2.2 The Random Batch Method for DMC -- References -- Trends in Consensus-Based Optimization -- 1 Introduction -- 1.1 Notation and Assumptions -- 1.1.1 The Weighted Average -- 2 Consensus-Based Global Optimization Methods -- 2.1 Original Statement of the Method -- 2.1.1 Particle Scheme -- 2.1.2 Mean-Field Limit -- 2.1.3 Analytical Results for the Original Scheme Without Heaviside Function -- 2.1.4 Numerical Methods -- 2.2 Variant 1: Component-Wise Diffusion and Random Batches -- 2.2.1 Component-Wise Geometric Brownian Motion -- 2.2.2 Random Batch Method -- 2.2.3 Implementation and Numerical Results -- 2.3 Variant 2: Component-Wise Common Diffusion -- 2.3.1 Analytical Results -- 2.3.2 Numerical Results -- 3 Relationship of CBO and Particle Swarm Optimization -- 3.1 Variant 4: Personal Best Information -- 3.1.1 Performance -- 4 CBO with State Constraints -- 4.1 Variant 5: Dynamics Constrained to Hyper-Surfaces -- 4.1.1 Analytical Results -- 5 Overview of Applications -- 5.1 Global Optimization Problems: Comparison to Heuristic Methods -- 5.2 Machine Learning -- 5.3 Global Optimization with Constrained State Space -- 5.4 PDE Versus SDE Simulations -- 6 Conclusion, Outlook and Open problems -- References. |
| Record Nr. | UNINA-9910556880003321 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced computing and systems for security . Volume 14 / / Rituparna Chaki [and four others], editors
| Advanced computing and systems for security . Volume 14 / / Rituparna Chaki [and four others], editors |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (213 pages) |
| Disciplina | 005.8 |
| Collana | Lecture notes in networks and systems |
| Soggetto topico |
Computer security
Seguretat informàtica Models matemàtics Mineria de dades Algorismes computacionals |
| Soggetto genere / forma |
Llibres electrònics
Congressos |
| ISBN | 981-16-4294-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910734097103321 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in computational methods and technologies in aeronautics and industry / / edited by Dietrich Knoerzer, Jacques Periaux, and Tero Tuovinen
| Advances in computational methods and technologies in aeronautics and industry / / edited by Dietrich Knoerzer, Jacques Periaux, and Tero Tuovinen |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (290 pages) |
| Disciplina | 551.48 |
| Collana | Computational Methods in Applied Sciences |
| Soggetto topico |
Aeronautics
Aeronàutica Models matemàtics Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-12019-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996503550903316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in computational methods and technologies in aeronautics and industry / / edited by Dietrich Knoerzer, Jacques Periaux, and Tero Tuovinen
| Advances in computational methods and technologies in aeronautics and industry / / edited by Dietrich Knoerzer, Jacques Periaux, and Tero Tuovinen |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (290 pages) |
| Disciplina | 551.48 |
| Collana | Computational Methods in Applied Sciences |
| Soggetto topico |
Aeronautics
Aeronàutica Models matemàtics Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-12019-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910634036003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in fuzzy group decision making / / Tin-Chih Toly Chen
| Advances in fuzzy group decision making / / Tin-Chih Toly Chen |
| Autore | Chen Toly <1969-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (94 pages) |
| Disciplina | 003.56 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Fuzzy decision making
Conjunts borrosos Decisió de grup Models matemàtics Group decision making - Mathematical models |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-86208-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910502611803321 |
Chen Toly <1969->
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in fuzzy group decision making / / Tin-Chih Toly Chen
| Advances in fuzzy group decision making / / Tin-Chih Toly Chen |
| Autore | Chen Toly <1969-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (94 pages) |
| Disciplina | 003.56 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Fuzzy decision making
Conjunts borrosos Decisió de grup Models matemàtics Group decision making - Mathematical models |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-86208-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466387303316 |
|
Chen Toly <1969->
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors
| Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (426 pages) |
| Disciplina | 511.8 |
| Soggetto topico |
Mathematical models
Simulation methods Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10193-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Biography -- Contents -- Monte Carlo Methods for Pricing American Options -- 1 Introduction -- 2 American Option Pricing -- 3 Binomial Tree Method -- 4 Dynamic Programming Approach -- 4.1 Regression Methods -- 4.2 Malliavin Calculus -- 5 Control Variates -- 6 Numerical Experiments -- 7 Conclusion -- References -- Remarks on Lévy Process Simulation -- 1 Introduction -- 2 Lévy Processes -- 3 Main Examples -- 4 The ε-Algorithm -- 5 Using Complete Monotonicity Structure -- 6 Numerical Examples -- 7 Exact Simulation of X(h) and other Methods -- 8 Maxima, Minima and Other Path Functionals -- References -- Exact Sampling for the Maximum of Infinite Memory Gaussian Processes -- 1 Introduction -- 2 Basic Strategy -- 2.1 Milestone Events -- 2.2 Main Algorithm -- 3 Intermediate Steps in Algorithm 2 -- 4 Analysis of Algorithm 2 -- 4.1 Output Analysis -- 4.2 Complexity Analysis -- 5 Numerical Experiments -- 6 Conclusion -- References -- Truncated Multivariate Student Computations via Exponential Tilting -- 1 Introduction -- 2 Review of the Sequentially Tilted Proposal Density -- 3 Asymptotic Efficiency of the IS Estimator -- 4 Application to Constrained Linear Regression -- 5 Tobit Model Application -- 6 Application to ``Bayesian'' Splines for Non-negative Functions -- 7 The Reject-Regenerate Sampler -- 7.1 Nummelin Splitting of Transition Kernel -- 7.2 Rare-Event Robustness -- 8 Concluding Remarks -- References -- Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints -- 1 Introduction -- 2 Classical Portfolio Selection in a Nutshell -- 3 Portfolio Optimization with Many Constraints -- 4 Approximation of the Opportunity Set by Naïve Monte Carlo, and by Exponential Monte Carlo -- 5 Approximation of the Opportunity Set with Exponential QMC.
6 Approximating the Market Portfolio with MC, Exponential MC, and Exponential QMC -- 7 Approximating the Whole OS with MC, Exponential MC, and Exponential QMC -- 8 How to Calculate the Dispersion of a Sample Set in an OS? -- 9 Some Simulation Results -- 10 Conclusions, Outlook, and Further Practical Problem -- References -- Geometric-Moment Contraction of G/G/1 Waiting Times -- 1 Introduction -- 2 Main Results -- 3 Monte Carlo Results -- 3.1 M/M/1 Queue -- 3.2 M/G/1 Queues -- 4 Conclusions -- References -- Tractability of Approximation in the Weighted Korobov Space in the Worst-Case Setting -- 1 Introduction -- 2 Basic Definitions -- 2.1 Function Space Setting -- 2.2 Approximation in script upper H Subscript d comma alpha comma bold italic gammamathcalHd,α,γ -- 2.3 The Worst-Case Setting -- 2.4 Useful Relations -- 2.5 Relations to the Average-Case Setting -- 2.6 Notions of Tractability -- 3 The Results for normal upper A normal upper P normal upper P Subscript 2APP2 -- 4 The Results for normal upper A normal upper P normal upper P Subscript normal infinityAPPinfty -- 5 Overview and Formulation of Open Problems -- 5.1 Open Problems -- References -- Rare-Event Simulation via Neural Networks -- 1 Introduction -- 1.1 Background -- 2 Rare-Event Deep Learning -- 2.1 Networks and Loss Functions -- 2.2 Kernel Density Estimation -- 2.3 Training Procedure -- 2.4 Rare-Event Distribution -- 3 Experimental Results -- 3.1 Learning Normal Distributions -- 3.2 Normal Distribution Rare-Events -- 3.3 Learning Sum of Exponential Distributions -- 4 Conclusions and Further Research -- References -- Preintegration is Not Smoothing When Monotonicity Fails -- 1 Introduction -- 1.1 Related Work -- 1.2 The Problem -- 1.3 Informative Examples -- 1.4 Outline of This Paper -- 2 Smoothness Theorems in dd Dimensions -- 3 A High-Dimensional Example -- 4 Conclusion -- References. Combined Derivative Estimators -- 1 Introduction -- 2 Derivative Estimation -- 2.1 Background -- 2.2 Combined Estimators -- 2.3 Second Derivatives -- 2.4 Finite Difference Estimators and IPA -- 2.5 IPA and Randomized Score Functions -- 2.6 LRM Singularities -- 2.7 Generalized Likelihood Ratio Method -- 3 A Barrier Option Example -- 3.1 The Option Pricing Setting -- 3.2 The Barrier Option -- 3.3 A Combined IPA-LRM Estimator of Wang et al. ch10wang -- 3.4 GLR as a Combined IPA-LRM Estimator -- 4 Approaching Continuous Time: Averaging Low-Rank GLR Estimators -- 4.1 Approximating Continuous-Time Sensitivities -- 4.2 Averaging GLR Estimators -- 5 Concluding Remarks -- References -- A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting -- 1 Introduction -- 2 A Quantile Central Limit Theorem -- 3 A Uniform CLT for 1-Dependent Sequences -- 4 A Quantile Central Limit Theorem for Harris Processes -- 5 The Validity of Non-overlapping Batch-Means Estimation -- 6 Sufficient Conditions -- References -- Simulation of Markov Chains with Continuous State Space by Using Simple Stratified and Sudoku Latin Square Sampling -- 1 Introduction -- 2 Markov Chain Simulation with Stratified Sampling -- 2.1 Classical Monte Carlo -- 2.2 Simple Stratified Sampling -- 2.3 Sudoku Latin Square Sampling -- 3 Variance Bounds -- 3.1 Classical Monte Carlo -- 3.2 Simple Stratified Sampling -- 3.3 Sudoku Latin Square Sampling -- 4 Numerical Experiments -- 4.1 An Autoregressive Process -- 4.2 A European Put Option -- 4.3 Diffusion -- 5 Conclusions -- References -- Quasi-Random Sampling with Black Box or Acceptance-Rejection Inputs -- 1 Introduction -- 2 Methods for the Black Box Setting -- 2.1 Methods Based on the Empirical Quantile Function -- 2.2 Methods Based on a Generalized Pareto Approximation in the Tail -- 3 Combining AR with RQMC. 4 Application: Basket Option Pricing -- 5 Conclusion -- References -- A Generalized Transformed Density Rejection Algorithm -- 1 Introduction -- 2 Transformed Density Rejection with Inflection Points -- 3 Determine Signs of Second Derivatives -- 3.1 Initial Intervals -- 3.2 Splitting Intervals -- 4 The Algorithm -- 5 Applications -- 5.1 Generalized Hyperbolic Distribution -- 5.2 Truncated Distributions -- 5.3 Watson Distributions -- 6 Conclusions -- References -- Fast Automatic Bayesian Cubature Using Sobol' Sampling -- 1 Introduction -- 2 Bayesian Cubature -- 3 Digital Nets and Walsh Kernels -- 3.1 Digital Sequences -- 3.2 Covariance Kernels Constructed Via Walsh Functions -- 3.3 Eigenvector-Eigenvalue Decomposition of the Gram Matrix -- 4 Numerical Experiments -- 4.1 Multivariate Gaussian Probability -- 4.2 Keister's Example -- 4.3 Asian Option Pricing -- 4.4 Discussion -- 5 Conclusion and Future Work -- References -- Rendering Along the Hilbert Curve -- 1 Introduction -- 2 Visual Error in Image Synthesis -- 3 Enumerating Pixels Along the Hilbert Curve -- 3.1 Correlation in Space-Filling Curves -- 3.2 Blue-Noise Dithered Sampling -- 4 Progressive Image Synthesis -- 4.1 Deterministic Cranley-Patterson Rotation -- 4.2 Randomization -- 4.3 Contiguous Segments of one Low Discrepancy Sequence -- 4.4 Partitioning one Low Discrepancy Sequence -- 5 Results and Discussion -- 6 Conclusion -- References -- Array-RQMC to Speed up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System -- 1 Introduction -- 2 Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set -- 2.1 Assumptions and Notations -- 2.2 Exponential Limit -- 2.3 Exponential Estimators with Monte Carlo (MC) -- 2.4 Convolution Estimators with Monte Carlo. 3 Array-RQMC Implementation of Regenerative-Simulation-Based Estimators of Quantiles -- 3.1 RQMC and Array-RQMC -- 3.2 Array-RQMC Exponential and Convolution Estimators -- 4 Numerical Illustration of the Gain on the Simulation of an M/M/1 Queue -- 5 Conclusions -- References -- Foundations of Ranking & -- Selection for Simulation Optimization -- 1 Introduction -- 2 Set Up -- 3 The Normal Means Case -- 3.1 The Indifference-Zone (IZ) Formulation -- 3.2 R& -- S Based on ``Statistical Learning'' -- 3.3 A Convergence-Rate Perspective -- 3.4 Doing Better Than ``Rate Optimal'' -- 3.5 Common Random Numbers -- 3.6 ``Good Selection'' -- 3.7 Unknown Variances -- 3.8 A Note on Asymptotic Analysis -- 4 Parallel R& -- S -- 4.1 New Measures of Efficiency -- 4.2 New Objectives -- 4.3 Parting Thoughts -- 5 Other Formulations -- 6 Multi-armed Bandits -- References -- Where are the Logs? -- 1 Introduction -- 2 Background -- 3 Proof of the Lower Bound -- 4 Discrepancy and the Case of d equals 1d=1 -- 5 Empirical Investigations for d equals 2d=2 -- 6 Very Large mm for Sobol' Nets -- 7 Discussion -- References -- Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo -- 1 Introduction -- 2 Performability Metrics and Resilience -- 2.1 The Resilience Metric -- 2.2 Some Properties of Resilience -- 3 Using Standard Monte Carlo for Resilience-Based Analysis -- 3.1 The Standard Estimator -- 3.2 The Standard Estimator Efficiently Implemented in the Rare Event Case -- 3.3 Estimating the Resilience -- 3.4 Improving Algorithm B -- 3.5 Sensitivity Analysis -- 4 Examples and Discussions -- 5 Conclusions -- References. |
| Record Nr. | UNINA-9910633937903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors
| Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (426 pages) |
| Disciplina | 511.8 |
| Soggetto topico |
Mathematical models
Simulation methods Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10193-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Biography -- Contents -- Monte Carlo Methods for Pricing American Options -- 1 Introduction -- 2 American Option Pricing -- 3 Binomial Tree Method -- 4 Dynamic Programming Approach -- 4.1 Regression Methods -- 4.2 Malliavin Calculus -- 5 Control Variates -- 6 Numerical Experiments -- 7 Conclusion -- References -- Remarks on Lévy Process Simulation -- 1 Introduction -- 2 Lévy Processes -- 3 Main Examples -- 4 The ε-Algorithm -- 5 Using Complete Monotonicity Structure -- 6 Numerical Examples -- 7 Exact Simulation of X(h) and other Methods -- 8 Maxima, Minima and Other Path Functionals -- References -- Exact Sampling for the Maximum of Infinite Memory Gaussian Processes -- 1 Introduction -- 2 Basic Strategy -- 2.1 Milestone Events -- 2.2 Main Algorithm -- 3 Intermediate Steps in Algorithm 2 -- 4 Analysis of Algorithm 2 -- 4.1 Output Analysis -- 4.2 Complexity Analysis -- 5 Numerical Experiments -- 6 Conclusion -- References -- Truncated Multivariate Student Computations via Exponential Tilting -- 1 Introduction -- 2 Review of the Sequentially Tilted Proposal Density -- 3 Asymptotic Efficiency of the IS Estimator -- 4 Application to Constrained Linear Regression -- 5 Tobit Model Application -- 6 Application to ``Bayesian'' Splines for Non-negative Functions -- 7 The Reject-Regenerate Sampler -- 7.1 Nummelin Splitting of Transition Kernel -- 7.2 Rare-Event Robustness -- 8 Concluding Remarks -- References -- Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints -- 1 Introduction -- 2 Classical Portfolio Selection in a Nutshell -- 3 Portfolio Optimization with Many Constraints -- 4 Approximation of the Opportunity Set by Naïve Monte Carlo, and by Exponential Monte Carlo -- 5 Approximation of the Opportunity Set with Exponential QMC.
6 Approximating the Market Portfolio with MC, Exponential MC, and Exponential QMC -- 7 Approximating the Whole OS with MC, Exponential MC, and Exponential QMC -- 8 How to Calculate the Dispersion of a Sample Set in an OS? -- 9 Some Simulation Results -- 10 Conclusions, Outlook, and Further Practical Problem -- References -- Geometric-Moment Contraction of G/G/1 Waiting Times -- 1 Introduction -- 2 Main Results -- 3 Monte Carlo Results -- 3.1 M/M/1 Queue -- 3.2 M/G/1 Queues -- 4 Conclusions -- References -- Tractability of Approximation in the Weighted Korobov Space in the Worst-Case Setting -- 1 Introduction -- 2 Basic Definitions -- 2.1 Function Space Setting -- 2.2 Approximation in script upper H Subscript d comma alpha comma bold italic gammamathcalHd,α,γ -- 2.3 The Worst-Case Setting -- 2.4 Useful Relations -- 2.5 Relations to the Average-Case Setting -- 2.6 Notions of Tractability -- 3 The Results for normal upper A normal upper P normal upper P Subscript 2APP2 -- 4 The Results for normal upper A normal upper P normal upper P Subscript normal infinityAPPinfty -- 5 Overview and Formulation of Open Problems -- 5.1 Open Problems -- References -- Rare-Event Simulation via Neural Networks -- 1 Introduction -- 1.1 Background -- 2 Rare-Event Deep Learning -- 2.1 Networks and Loss Functions -- 2.2 Kernel Density Estimation -- 2.3 Training Procedure -- 2.4 Rare-Event Distribution -- 3 Experimental Results -- 3.1 Learning Normal Distributions -- 3.2 Normal Distribution Rare-Events -- 3.3 Learning Sum of Exponential Distributions -- 4 Conclusions and Further Research -- References -- Preintegration is Not Smoothing When Monotonicity Fails -- 1 Introduction -- 1.1 Related Work -- 1.2 The Problem -- 1.3 Informative Examples -- 1.4 Outline of This Paper -- 2 Smoothness Theorems in dd Dimensions -- 3 A High-Dimensional Example -- 4 Conclusion -- References. Combined Derivative Estimators -- 1 Introduction -- 2 Derivative Estimation -- 2.1 Background -- 2.2 Combined Estimators -- 2.3 Second Derivatives -- 2.4 Finite Difference Estimators and IPA -- 2.5 IPA and Randomized Score Functions -- 2.6 LRM Singularities -- 2.7 Generalized Likelihood Ratio Method -- 3 A Barrier Option Example -- 3.1 The Option Pricing Setting -- 3.2 The Barrier Option -- 3.3 A Combined IPA-LRM Estimator of Wang et al. ch10wang -- 3.4 GLR as a Combined IPA-LRM Estimator -- 4 Approaching Continuous Time: Averaging Low-Rank GLR Estimators -- 4.1 Approximating Continuous-Time Sensitivities -- 4.2 Averaging GLR Estimators -- 5 Concluding Remarks -- References -- A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting -- 1 Introduction -- 2 A Quantile Central Limit Theorem -- 3 A Uniform CLT for 1-Dependent Sequences -- 4 A Quantile Central Limit Theorem for Harris Processes -- 5 The Validity of Non-overlapping Batch-Means Estimation -- 6 Sufficient Conditions -- References -- Simulation of Markov Chains with Continuous State Space by Using Simple Stratified and Sudoku Latin Square Sampling -- 1 Introduction -- 2 Markov Chain Simulation with Stratified Sampling -- 2.1 Classical Monte Carlo -- 2.2 Simple Stratified Sampling -- 2.3 Sudoku Latin Square Sampling -- 3 Variance Bounds -- 3.1 Classical Monte Carlo -- 3.2 Simple Stratified Sampling -- 3.3 Sudoku Latin Square Sampling -- 4 Numerical Experiments -- 4.1 An Autoregressive Process -- 4.2 A European Put Option -- 4.3 Diffusion -- 5 Conclusions -- References -- Quasi-Random Sampling with Black Box or Acceptance-Rejection Inputs -- 1 Introduction -- 2 Methods for the Black Box Setting -- 2.1 Methods Based on the Empirical Quantile Function -- 2.2 Methods Based on a Generalized Pareto Approximation in the Tail -- 3 Combining AR with RQMC. 4 Application: Basket Option Pricing -- 5 Conclusion -- References -- A Generalized Transformed Density Rejection Algorithm -- 1 Introduction -- 2 Transformed Density Rejection with Inflection Points -- 3 Determine Signs of Second Derivatives -- 3.1 Initial Intervals -- 3.2 Splitting Intervals -- 4 The Algorithm -- 5 Applications -- 5.1 Generalized Hyperbolic Distribution -- 5.2 Truncated Distributions -- 5.3 Watson Distributions -- 6 Conclusions -- References -- Fast Automatic Bayesian Cubature Using Sobol' Sampling -- 1 Introduction -- 2 Bayesian Cubature -- 3 Digital Nets and Walsh Kernels -- 3.1 Digital Sequences -- 3.2 Covariance Kernels Constructed Via Walsh Functions -- 3.3 Eigenvector-Eigenvalue Decomposition of the Gram Matrix -- 4 Numerical Experiments -- 4.1 Multivariate Gaussian Probability -- 4.2 Keister's Example -- 4.3 Asian Option Pricing -- 4.4 Discussion -- 5 Conclusion and Future Work -- References -- Rendering Along the Hilbert Curve -- 1 Introduction -- 2 Visual Error in Image Synthesis -- 3 Enumerating Pixels Along the Hilbert Curve -- 3.1 Correlation in Space-Filling Curves -- 3.2 Blue-Noise Dithered Sampling -- 4 Progressive Image Synthesis -- 4.1 Deterministic Cranley-Patterson Rotation -- 4.2 Randomization -- 4.3 Contiguous Segments of one Low Discrepancy Sequence -- 4.4 Partitioning one Low Discrepancy Sequence -- 5 Results and Discussion -- 6 Conclusion -- References -- Array-RQMC to Speed up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System -- 1 Introduction -- 2 Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set -- 2.1 Assumptions and Notations -- 2.2 Exponential Limit -- 2.3 Exponential Estimators with Monte Carlo (MC) -- 2.4 Convolution Estimators with Monte Carlo. 3 Array-RQMC Implementation of Regenerative-Simulation-Based Estimators of Quantiles -- 3.1 RQMC and Array-RQMC -- 3.2 Array-RQMC Exponential and Convolution Estimators -- 4 Numerical Illustration of the Gain on the Simulation of an M/M/1 Queue -- 5 Conclusions -- References -- Foundations of Ranking & -- Selection for Simulation Optimization -- 1 Introduction -- 2 Set Up -- 3 The Normal Means Case -- 3.1 The Indifference-Zone (IZ) Formulation -- 3.2 R& -- S Based on ``Statistical Learning'' -- 3.3 A Convergence-Rate Perspective -- 3.4 Doing Better Than ``Rate Optimal'' -- 3.5 Common Random Numbers -- 3.6 ``Good Selection'' -- 3.7 Unknown Variances -- 3.8 A Note on Asymptotic Analysis -- 4 Parallel R& -- S -- 4.1 New Measures of Efficiency -- 4.2 New Objectives -- 4.3 Parting Thoughts -- 5 Other Formulations -- 6 Multi-armed Bandits -- References -- Where are the Logs? -- 1 Introduction -- 2 Background -- 3 Proof of the Lower Bound -- 4 Discrepancy and the Case of d equals 1d=1 -- 5 Empirical Investigations for d equals 2d=2 -- 6 Very Large mm for Sobol' Nets -- 7 Discussion -- References -- Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo -- 1 Introduction -- 2 Performability Metrics and Resilience -- 2.1 The Resilience Metric -- 2.2 Some Properties of Resilience -- 3 Using Standard Monte Carlo for Resilience-Based Analysis -- 3.1 The Standard Estimator -- 3.2 The Standard Estimator Efficiently Implemented in the Rare Event Case -- 3.3 Estimating the Resilience -- 3.4 Improving Algorithm B -- 3.5 Sensitivity Analysis -- 4 Examples and Discussions -- 5 Conclusions -- References. |
| Record Nr. | UNISA-996499866203316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Algorithms for a New World [[electronic resource] ] : When Big Data and Mathematical Models Meet / / by Alfio Quarteroni
| Algorithms for a New World [[electronic resource] ] : When Big Data and Mathematical Models Meet / / by Alfio Quarteroni |
| Autore | Quarteroni Alfio |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (68 pages) : illustrations |
| Disciplina | 006.3 |
| Soggetto topico |
Mathematics
Machine learning Artificial intelligence Quantitative research Algorithms Applications of Mathematics Machine Learning Artificial Intelligence Data Analysis and Big Data Models matemàtics Intel·ligència artificial Dades massives |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030961664
9783030961657 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Epidemic -- 2 Retrospective -- 3 Interlude: the revolution that did not happen and the revolution that was unforeseen -- 4 Artificial intelligence, learning computers, artificial neural networks -- 5 A bit of maths (behind artificial intelligence and machine learning) -- 6 BIG DATA - BIG BROTHER (or, on the ethical and moral aspects of artificial intelligence). |
| Record Nr. | UNISA-996483155303316 |
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Quarteroni Alfio
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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