Numerical computations -- theory and algorithms : third International Conference, NUMTA 2019, Crotone, Italy, June 15-21, 2019, Revised Selected Papers. Part II

Numerical computations -- theory and algorithms : Part II / third International Conference, NUMTA 2019, Crotone, Italy, June 15-21, 2019, Revised Selected Papers. Yaroslav D. Sergeyev, Dmitri E. Kvasov (eds.).

The two-volume set LNCS 11973 and 11974 constitute revised selected papers from the Third International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2019, held in Crotone, Italy, in June 2019. This volume, LNCS 11974, consists of 19 full and 32 short papers chosen among regular...

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Bibliographic Details
Corporate Author: NUMTA (Conference) Crotone, Italy)
Other Authors: Sergeyev, Yaroslav D., 1963-, Kvasov, Dmitri E., 1977-
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, [2020]
Series:Lecture notes in computer science ; 11974.
LNCS sublibrary. Theoretical computer science and general issues.
Subjects:
Numerical analysis > Congresses.
Numerical analysis
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Part II
  • Contents
  • Part I
  • Long Papers
  • Numerical Algorithms for the Parametric Continuation of Stiff ODEs Derivingpg from the Modeling of Combustionpg with Detailed Chemical Mechanisms
  • 1 Introduction
  • 2 Mathematical Problem Setup
  • 3 Test Functions
  • 4 Study Case
  • 5 Conclusions
  • References
  • Stability Analysis of DESA Optimization Algorithm
  • 1 Introduction
  • 2 The DESA Algorithm
  • 2.1 The Implementation of the DESA Algorithms
  • 2.2 The Binomial Crossover Operator of DESA Algorithm
  • 2.3 The SA-like Selection Operator of DESA Algorithm
  • 2.4 The Mean and Variance of the DESA-population
  • 3 Stability Analysis of the DESA Population System
  • 4 The Learning Rate and Momentum of DESA-population
  • 4.1 Comparison of Learning Rate and Momentum Rate of de and DESA
  • 4.2 On the Stability of DESA in the Sense of Lyapunov
  • 5 Conclusions
  • References
  • Are Humans Bayesian in the Optimization of Black-Box Functions?
  • 1 Introduction
  • 2 Methodological Background
  • 2.1 Gaussian Processes
  • 2.2 GP-Based Optimization
  • 2.3 Bayesian Optimization
  • 3 Experimental Setup
  • 3.1 Test Function as a "Task"
  • 3.2 Experiment: Optimization by Humans
  • 4 Conclusions
  • References
  • A New Syntax for Diagrammatic Logic: A Generic Figures Approach
  • 1 Introduction
  • 2 Peirce's Existential Graphs
  • 2.1 Existential Graphs: Alpha
  • 2.2 Existential Graphs: Beta
  • 3 Presheaf Categories and Their Generic Figures
  • 3.1 Category Theory: Basic Notions
  • 3.2 Functors Between Categories
  • 3.3 Presheaf Categories and Generic Figures
  • 4 Existential Graphs as Functor Categories
  • 4.1 EG
  • 4.2 EG
  • 4.3 EG+
  • 4.4 EG
  • 5 Conclusion
  • References
  • Objective and Violation Upper Bounds on a DIRECT-Filter Method for Global Optimization
  • 1 Introduction
  • 2 Features About DIRECT Method
  • 3 DIRECT-Filter Method
  • 3.1 Filter Methodology
  • 3.2 Identifying POH in the DIRECT-Filter Method
  • 3.3 Objective and Violation Upper Bounds
  • 3.4 Heuristic
  • 4 Numerical Experiments
  • 5 Conclusions
  • References
  • The Approximate Synthesis of Optimal Control for Heterogeneous Discrete Systems with Intermediate Criteria
  • 1 Introduction
  • 2 Heterogeneous Discrete Processes with Intermediate Criteria
  • 3 Sufficient Optimality Conditions
  • 4 Sufficient Conditions in the Bellman Form
  • 5 The Approximate Synthesis of Optimal Control
  • 6 Iterative Procedure
  • 7 Example
  • 8 Conclusion
  • References
  • Modelling Climate Changes with Stationary Models: Is It Possible or Is It a Paradox?
  • 1 Introduction
  • 2 Methods and Materials
  • 2.1 Brief Overview of NSRP Model
  • 2.2 The Case Study of Viterbo Raingauge
  • 3 Results and Discussion
  • 3.1 Calibration of NSRP Models Without Parameter Trend
  • 3.2 Results from the Assumed Parameter Trends
  • 4 Conclusions
  • References

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