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End User License Agreement
by John C. Nash
Nonlinear Parameter Optimization Using R Tools
Cover
Title Page
Copyright
Dedication
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
1.5 Constraint types
1.6 Solving sets of equations
1.7 Conditions for optimality
1.8 Other classifications
References
Chapter 2: Optimization algorithms—an overview
2.1 Methods that use the gradient
2.2 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
2.11 Stochastic methods
2.12 Constraint-based methods—mathematical programming
References
Chapter 3: Software structure and interfaces
3.1 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
3.6 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
3.13 Output to monitor progress of calculations
3.14 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
4.4 Approaches to solving 1D root-finding problems
4.5 What can go wrong?
4.6 Being a smart user of root-finding programs
4.7 Conclusions and extensions
References
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
6.2 A more difficult case
6.3 The structure of the nls() solution
6.4 Concerns with nls()
6.5 Some ancillary tools for nonlinear least squares
6.6 Minimizing R functions that compute sums of squares
6.7 Choosing an approach
6.8 Separable sums of squares problems
6.9 Strategies for nonlinear least squares
References
Chapter 7: Nonlinear equations
7.1 Packages and methods for nonlinear equations
7.2 A simple example to compare approaches
7.3 A statistical example
References
Chapter 8: Function minimization tools in the base R system
8.1 optim()
8.2 nlm()
8.3 nlminb()
8.4 Using the base optimization tools
References
Chapter 9: Add-in function minimization packages for R
9.1 Package optimx
9.2 Some other function minimization packages
9.3 Should we replace optim() routines?
References
Chapter 10: Calculating and using derivatives
10.1 Why and how
10.2 Analytic derivatives—by hand
10.3 Analytic derivatives—tools
10.4 Examples of use of R tools for differentiation
10.5 Simple numerical derivatives
10.6 Improved numerical derivative approximations
10.7 Strategy and tactics for derivatives
References
Chapter 11: Bounds constraints
11.1 Single bound: use of a logarithmic transformation
11.2 Interval bounds: Use of a hyperbolic transformation
11.3 Setting the objective large when bounds are violated
11.4 An active set approach
11.5 Checking bounds
11.6 The importance of using bounds intelligently
11.7 Post-solution information for bounded problems
Appendix 11.A Function transfinite
References
Chapter 12: Using masks
12.1 An example
12.2 Specifying the objective
12.3 Masks for nonlinear least squares
12.4 Other approaches to masks
References
Chapter 13: Handling general constraints
13.1 Equality constraints
13.2 Sumscale problems
13.3 Inequality constraints
13.4 A perspective on penalty function ideas
13.5 Assessment
References
Chapter 14: Applications of mathematical programming
14.1 Statistical applications of math programming
14.2 R packages for math programming
14.3 Example problem: L1 regression
14.4 Example problem: minimax regression
14.5 Nonlinear quantile regression
14.6 Polynomial approximation
References
Chapter 15: Global optimization and stochastic methods
15.1 Panorama of methods
15.2 R packages for global and stochastic optimization
15.3 An example problem
15.4 Multiple starting values
References
Chapter 16: Scaling and reparameterization
16.1 Why scale or reparameterize?
16.2 Formalities of scaling and reparameterization
16.3 Hobbs' weed infestation example
16.4 The KKT conditions and scaling
16.5 Reparameterization of the weeds problem
16.6 Scale change across the parameter space
16.7 Robustness of methods to starting points
16.8 Strategies for scaling
References
Chapter 17: Finding the right solution
17.1 Particular requirements
17.2 Starting values for iterative methods
17.3 KKT conditions
17.4 Search tests
References
Chapter 18: Tuning and terminating methods
18.1 Timing and profiling
18.2 Profiling
18.3 More speedups of R computations
18.4 External language compiled functions
18.5 Deciding when we are finished
References
Chapter 19: Linking R to external optimization tools
19.1 Mechanisms to link R to external software
19.2 Prepackaged links to external optimization tools
19.3 Strategy for using external tools
References
Chapter 20: Differential equation models
20.1 The model
20.2 Background
20.3 The likelihood function
20.4 A first try at minimization
20.5 Attempts with optimx
20.6 Using nonlinear least squares
20.7 Commentary
Reference
Chapter 21: Miscellaneous nonlinear estimation tools for R
21.1 Maximum likelihood
21.2 Generalized nonlinear models
21.3 Systems of equations
21.4 Additional nonlinear least squares tools
21.5 Nonnegative least squares
21.6 Noisy objective functions
21.7 Moving forward
References
Appendix A: R packages used in examples
Index
End User License Agreement
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