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Global Optimization in Action Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications

In science, engineering and economics, decision problems are frequently modelled by optimizing the value of a (primary) objective function under stated feasibility constraints. In many cases of practical relevance, the optimization problem structure does not warrant the global optimality of local so...

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Bibliographic Details
Main Author: Pintér, János D.
Format: eBook
Language:English
Published: New York, NY Springer US 1996, 1996
Edition:1st ed. 1996
Series:Nonconvex Optimization and Its Applications
Subjects:
Optimization
Control Theory
Systems Theory, Control
System Theory
Mathematical Modeling And Industrial Mathematics
Applications Of Mathematics
Mathematics
Mathematical Optimization
Mathematical Models
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • One Global Optimization: A Brief Review
  • 1.1 General Problem Statement and Special Model Forms
  • 1.2 Solution Approaches
  • Two Partition Strategies in Global Optimization: The Continuous and the Lipschitzian Case
  • 2.1 An Introduction to Partition Algorithms
  • 2.2 Convergence Properties of Adaptive Partition Algorithms
  • 2.3 Partition Algorithms on Intervals
  • 2.4 Partition Algorithms on Multidimensional Intervals
  • 2.5 Simplex Partition Strategies
  • 2.6 Partition Methods on General Convex and Star Sets
  • 2.7 Partition Strategies in General Lipschitz Optimization
  • Three Implementation Aspects, Algorithm Modifications and Stochastic Extensions
  • 3.1 Diagonally Extended Univariate Algorithms for Multidimensional Global Optimization
  • 3.2 Estimation of Lipschitzian Problem Characteristics in Global Optimization
  • 3.3 General Lipschitz Optimization Applying Penalty Multipliers
  • 3.4 An Implementation of a Lipschitzian Global Optimization Procedure
  • 3.5 Decision Making under Uncertainty:Stochastic Model Forms
  • 3.6 Adaptive Stochastic Optimization Procedures
  • 3.7 Estimation of Noise-Perturbed Function Values
  • Four Applications
  • 4.1 Nonlinear Approximation: Systems of Equations and Inequalities
  • 4.2 Data Classification (Clustering) and Related Problems
  • 4.3 Aggregation of Negotiated Expert Opinions
  • 4.4 Product (Mixture) Design
  • 4.5 Globally Optimized Calibration of Complex System Models
  • 4.6 Calibration Model Versions, Illustrated by Examples
  • 4.7 Dynamic Modelling of Phosphorus Release from Sediments
  • 4.8 Aquifer Model Calibration
  • 4.9 Industrial Wastewater Management
  • 4.10 Multiple Source River Pollution Management
  • 4.11 Lake Eutrophication Management
  • 4.12 Risk Management of Accidental Water Pollution
  • Afterword
  • Some Further Research Perspectives
  • References

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