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Nonlinear parameter optimization using R tools /
The aim of this book is to provide an appreciation of the R tools available for optimization problems. Most users of R are not specialists in computation and the workings of the specialized tools are a black box. This can lead to mis-application, therefore users need help in making appropriate choic...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex, United Kingdom :
John Wiley & Sons, Inc.,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Nash, John C., |d 1947- | |
| 245 | 1 | 0 | |a Nonlinear parameter optimization using R tools / |c John C. Nash, Telfer School of Management, University of Ottawa. |
| 264 | 1 | |a Chichester, West Sussex, United Kingdom : |b John Wiley & Sons, Inc., |c 2014. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Preface; Chapter 1 Optimization problem tasks and how they arise; 1.1 The general optimization problem; 1.2 Why the general problem is generally uninteresting; 1.3 (Non- )Linearity; 1.4 Objective function properties; 1.4.1 Sums of squares; 1.4.2 Minimax approximation; 1.4.3 Problems with multiple minima; 1.4.4 Objectives that can only be imprecisely computed; 1.5 Constraint types; 1.6 Solving sets of equations; 1.7 Conditions for optimality; 1.8 Other classifications; References; Chapter 2 Optimization algorithms-an overview. | |
| 505 | 8 | |a 2.1 Methods that use the gradient2.2 Newton-like methods; 2.3 The promise of Newton's method; 2.4 Caution: convergence versus termination; 2.5 Difficulties with Newton's method; 2.6 Least squares: Gauss-Newton methods; 2.7 Quasi-Newton or variable metric method; 2.8 Conjugate gradient and related methods; 2.9 Other gradient methods; 2.10 Derivative-free methods; 2.10.1 Numerical approximation of gradients; 2.10.2 Approximate and descend; 2.10.3 Heuristic search; 2.11 Stochastic methods; 2.12 Constraint-based methods-mathematical programming; References. | |
| 505 | 8 | |a Chapter 3 Software structure and interfaces3.1 Perspective; 3.2 Issues of choice; 3.3 Software issues; 3.4 Specifying the objective and constraints to the optimizer; 3.5 Communicating exogenous data to problem definition functions; 3.5.1 Use of ""global'' data and variables; 3.6 Masked (temporarily fixed) optimization parameters; 3.7 Dealing with inadmissible results; 3.8 Providing derivatives for functions; 3.9 Derivative approximations when there are constraints; 3.10 Scaling of parameters and function; 3.11 Normal ending of computations; 3.12 Termination tests-abnormal ending. | |
| 505 | 8 | |a 3.13 Output to monitor progress of calculations3.14 Output of the optimization results; 3.15 Controls for the optimizer; 3.16 Default control settings; 3.17 Measuring performance; 3.18 The optimization interface; References; Chapter 4 One-parameter root-finding problems; 4.1 Roots; 4.2 Equations in one variable; 4.3 Some examples; 4.3.1 Exponentially speaking; 4.3.2 A normal concern; 4.3.3 Little Polly Nomial; 4.3.4 A hypothequial question; 4.4 Approaches to solving 1D root-finding problems; 4.5 What can go wrong?; 4.6 Being a smart user of root-finding programs. | |
| 505 | 8 | |a 4.7 Conclusions and extensionsReferences; Chapter 5 One-parameter minimization problems; 5.1 The optimize() function; 5.2 Using a root-finder; 5.3 But where is the minimum?; 5.4 Ideas for 1D minimizers; 5.5 The line-search subproblem; References; Chapter 6 Nonlinear least squares; 6.1 nls() from package stats; 6.1.1 A simple example; 6.1.2 Regression versus least squares; 6.2 A more difficult case; 6.3 The structure of the nls() solution; 6.4 Concerns with nls(); 6.4.1 Small residuals; 6.4.2 Robustness-""singular gradient'' woes; 6.4.3 Bounds with nls(). | |
| 520 | |a The aim of this book is to provide an appreciation of the R tools available for optimization problems. Most users of R are not specialists in computation and the workings of the specialized tools are a black box. This can lead to mis-application, therefore users need help in making appropriate choices. This book looks at the principal tools available for users of the R statistical computing system for function minimization, optimization, and nonlinear parameter determination, featuring numerous examples throughout. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Nonlinear theories. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 6 | |a Théories non linéaires. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 650 | 7 | |a Nonlinear theories |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 758 | |i has work: |a Nonlinear parameter optimization using R tools (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFv39pKcVymhHG6QyXJDpX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Nash, John C., 1947- |t Nonlinear parameter optimization using R tools. |d Chichester, West Sussex : John Wiley & Sons, Inc., 2014 |z 9781118569283 |w (DLC) 2013051141 |
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| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781118883969/?ar |z Texto completo |
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