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Nonlinear Parameter Optimization Using R Tools
About this book
Nonlinear Parameter Optimization Using R
John C. Nash, Telfer School of Management, University of Ottawa, Canada
A systematic and comprehensive treatment of optimization software using R
In recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under non–linear, multivariable conditions, more quickly than ever before.
Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout.
Nonlinear Parameter Optimization with R:
- Provides a comprehensive treatment of optimization techniques
- Examines optimization problems that arise in statistics and how to solve them using R
- Enables researchers and practitioners to solve parameter determination problems
- Presents traditional methods as well as recent developments in R
- Is supported by an accompanying website featuring R code, examples and datasets
Researchers and practitioners who have to solve parameter determination problems who are users of R but are novices in the field optimization or function minimization will benefit from this book. It will also be useful for scientists building and estimating nonlinear models in various fields such as hydrology, sports forecasting, ecology, chemical engineering, pharmaco-kinetics, agriculture, economics and statistics.
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Information
Table of contents
- Cover
- Title Page
- Copyright
- Dedication
- Preface
- Chapter 1: Optimization problem tasks and how they arise
- Chapter 2: Optimization algorithms—an overview
- Chapter 3: Software structure and interfaces
- Chapter 4: One-parameter root-finding problems
- Chapter 5: One-parameter minimization problems
- Chapter 6: Nonlinear least squares
- Chapter 7: Nonlinear equations
- Chapter 8: Function minimization tools in the base R system
- Chapter 9: Add-in function minimization packages for R
- Chapter 10: Calculating and using derivatives
- Chapter 11: Bounds constraints
- Chapter 12: Using masks
- Chapter 13: Handling general constraints
- Chapter 14: Applications of mathematical programming
- Chapter 15: Global optimization and stochastic methods
- Chapter 16: Scaling and reparameterization
- Chapter 17: Finding the right solution
- Chapter 18: Tuning and terminating methods
- Chapter 19: Linking R to external optimization tools
- Chapter 20: Differential equation models
- Chapter 21: Miscellaneous nonlinear estimation tools for R
- Appendix A: R packages used in examples
- Index
- End User License Agreement