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.

Table of Contents:
Preface xv 1 Optimization problem tasks and how they arise 1 1.1 The general optimization problem 1 1.2 Why the general problem is generally uninteresting 2 1.3 (Non-)Linearity 4 1.4 Objective function properties 4 1.4.1 Sums of squares 4 1.4.2 Minimax approximation 5 1.4.3 Problems with multiple minima 5 1.4.4 Objectives that can only be imprecisely computed 5 1.5 Constraint types 5 1.6 Solving sets of equations 6 1.7 Conditions for optimality 7 1.8 Other classifications 7 References 8 2 Optimization algorithms – an overview 9 2.1 Methods that use the gradient 9 2.2 Newton-like methods 12 2.3 The promise of Newton’s method 13 2.4 Caution: convergence versus termination 14 2.5 Difficulties with Newton’s method 14 2.6 Least squares: Gauss–Newton methods 15 2.7 Quasi-Newton or variable metric method 17 2.8 Conjugate gradient and related methods 18 2.9 Other gradient methods 19 2.10 Derivative-free methods 19 2.10.1 Numerical approximation of gradients 19 2.10.2 Approximate and descend 19 2.10.3 Heuristic search 20 2.11 Stochastic methods 20 2.12 Constraint-based methods – mathematical programming 21 References 22 3 Software structure and interfaces 25 3.1 Perspective 25 3.2 Issues of choice 26 3.3 Software issues 27 3.4 Specifying the objective and constraints to the optimizer 28 3.5 Communicating exogenous data to problem definition functions 28 3.5.1 Use of “global” data and variables 31 3.6 Masked (temporarily fixed) optimization parameters 32 3.7 Dealing with inadmissible results 33 3.8 Providing derivatives for functions 34 3.9 Derivative approximations when there are constraints 36 3.10 Scaling of parameters and function 36 3.11 Normal ending of computations 36 3.12 Termination tests – abnormal ending 37 3.13 Output to monitor progress of calculations 37 3.14 Output of the optimization results 38 3.15 Controls for the optimizer 38 3.16 Default control settings 39 3.17 Measuring performance 39 3.18 The optimization interface 39 References 40 4 One-parameter root-finding problems 41 4.1 Roots 41 4.2 Equations in one variable 42 4.3 Some examples 42 4.3.1 Exponentially speaking 42 4.3.2 A normal concern 44 4.3.3 Little Polly Nomial 46 4.3.4 A hypothequial question 49 4.4 Approaches to solving 1D root-finding problems 51 4.5 What can go wrong? 52 4.6 Being a smart user of root-finding programs 54 4.7 Conclusions and extensions 54 References 55 5 One-parameter minimization problems 56 5.1 The optimize() function 56 5.2 Using a root-finder 57 5.3 But where is the minimum? 58 5.4 Ideas for 1D minimizers 59 5.5 The line-search subproblem 61 References 62 6 Nonlinear least squares 63 6.1 nls() from package stats 63 6.1.1 A simple example 63 6.1.2 Regression versus least squares 65 6.2 A more difficult case 65 6.3 The structure of the nls() solution 72 6.4 Concerns with nls() 73 6.4.1 Small residuals 74 6.4.2 Robustness – “singular gradient” woes 75 6.4.3 Bounds with nls() 77 6.5 Some ancillary tools for nonlinear least squares 79 6.5.1 Starting values and self-starting problems 79 6.5.2 Converting model expressions to sum-of-squares functions 80 6.5.3 Help for nonlinear regression 80 6.6 Minimizing Rfunctions that compute sums of squares 81 6.7 Choosing an approach 82 6.8 Separable sums of squares problems 86 6.9 Strategies for nonlinear least squares 93 References 93 7 Nonlinear equations 95 7.1 Packages and methods for nonlinear equations 95 7.1.1 BB 96 7.1.2 nleqslv 96 7.1.3 Using nonlinear least squares 96 7.1.4 Using function minimization methods 96 7.2 A simple example to compare approaches 97 7.3 A statistical example 103 References 106 8 Function minimization tools in the base R system 108 8.1 optim() 108 8.2 nlm() 110 8.3 nlminb() 111 8.4 Using the base optimization tools 112 References 114 9 Add-in function minimization packages for R 115 9.1 Package optimx 115 9.1.1 Optimizers in optimx 116 9.1.2 Example use of optimx() 117 9.2 Some other function minimization packages 118 9.2.1 nloptr and nloptwrap 118 9.2.2 trust and trustOptim 119 9.3 Should we replace optim() routines? 121 References 122 10 Calculating and using derivatives 123 10.1 Why and how 123 10.2 Analytic derivatives – by hand 124 10.3 Analytic derivatives – tools 125 10.4 Examples of use of R tools for differentiation 125 10.5 Simple numerical derivatives 127 10.6 Improved numerical derivative approximations 128 10.6.1 The Richardson extrapolation 128 10.6.2 Complex-step derivative approximations 128 10.7 Strategy and tactics for derivatives 129 References 131 11 Bounds constraints 132 11.1 Single bound: use of a logarithmic transformation 132 11.2 Interval bounds: Use of a hyperbolic transformation 133 11.2.1 Example of the tanh transformation 134 11.2.2 A fly in the ointment 134 11.3 Setting the objective large when bounds are violated 135 11.4 An active set approach 136 11.5 Checking bounds 138 11.6 The importance of using bounds intelligently 138 11.6.1 Difficulties in applying bounds constraints 139 11.7 Post-solution information for bounded problems 139 Appendix 11.A Function transfinite 141 References 142 12 Using masks 143 12.1 An example 143 12.2 Specifying the objective 143 12.3 Masks for nonlinear least squares 147 12.4 Other approaches to masks 148 References 148 13 Handling general constraints 149 13.1 Equality constraints 149 13.1.1 Parameter elimination 151 13.1.2 Which parameter to eliminate? 153 13.1.3 Scaling and centering? 154 13.1.4 Nonlinear programming packages 154 13.1.5 Sequential application of an increasing penalty 156 13.2 Sumscale problems 158 13.2.1 Using a projection 162 13.3 Inequality constraints 163 13.4 A perspective on penalty function ideas 167 13.5 Assessment 167 References 168 14 Applications of mathematical programming 169 14.1 Statistical applications of math programming 169 14.2 R packages for math programming 170 14.3 Example problem: L1 regression 171 14.4 Example problem: minimax regression 177 14.5 Nonlinear quantile regression 179 14.6 Polynomial approximation 180 References 183 15 Global optimization and stochastic methods 185 15.1 Panorama of methods 185 15.2 R packages for global and stochastic optimization 186 15.3 An example problem 187 15.3.1 Method SANN from optim() 187 15.3.2 Package GenSA 188 15.3.3 Packages DEoptim and RcppDE 189 15.3.4 Package smco 191 15.3.5 Package soma 192 15.3.6 Package Rmalschains 193 15.3.7 Package rgenoud 193 15.3.8 Package GA 194 15.3.9 Package gaoptim 195 15.4 Multiple starting values 196 References 202 16 Scaling and reparameterization 203 16.1 Why scale or reparameterize? 203 16.2 Formalities of scaling and reparameterization 204 16.3 Hobbs’ weed infestation example 205 16.4 The KKT conditions and scaling 210 16.5 Reparameterization of the weeds problem 214 16.6 Scale change across the parameter space 214 16.7 Robustness of methods to starting points 215 16.7.1 Robustness of optimization techniques 218 16.7.2 Robustness of nonlinear least squares methods 220 16.8 Strategies for scaling 222 References 223 17 Finding the right solution 224 17.1 Particular requirements 224 17.1.1 A few integer parameters 225 17.2 Starting values for iterative methods 225 17.3 KKT conditions 226 17.3.1 Unconstrained problems 226 17.3.2 Constrained problems 227 17.4 Search tests 228 References 229 18 Tuning and terminating methods 230 18.1 Timing and profiling 230 18.1.1 rbenchmark 231 18.1.2 microbenchmark 231 18.1.3 Calibrating our timings 232 18.2 Profiling 234 18.2.1 Trying possible improvements 235 18.3 More speedups of R computations 238 18.3.1 Byte-code compiled functions 238 18.3.2 Avoiding loops 238 18.3.3 Package upgrades - an example 239 18.3.4 Specializing codes 241 18.4 External language compiled functions 242 18.4.1 Building an R function using Fortran 244 18.4.2 Summary of Rayleigh quotient timings 246 18.5 Deciding when we are finished 247 18.5.1 Tests for things gone wrong 248 References 249 19 Linking R to external optimization tools 250 19.1 Mechanisms to link R to external software 251 19.1.1 R functions to call external (sub)programs 251 19.1.2 File and system call methods 251 19.1.3 Thin client methods 252 19.2 Prepackaged links to external optimization tools 252 19.2.1 NEOS 252 19.2.2 Automatic Differentiation Model Builder (ADMB) 252 19.2.3 NLopt 253 19.2.4 BUGS and related tools 253 19.3 Strategy for using external tools 253 References 254 20 Differential equation models 255 20.1 The model 255 20.2 Background 256 20.3 The likelihood function 258 20.4 A first try at minimization 258 20.5 Attempts with optimx 259 20.6 Using nonlinear least squares 260 20.7 Commentary 261 Reference 262 21 Miscellaneous nonlinear estimation tools for R 263 21.1 Maximum likelihood 263 21.2 Generalized nonlinear models 266 21.3 Systems of equations 268 21.4 Additional nonlinear least squares tools 268 21.5 Nonnegative least squares 270 21.6 Noisy objective functions 273 21.7 Moving forward 274 References 275 Appendix A R packages used in examples 276 Index 279