# GSL nonlinear least squares fitting in R

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# Introduction

The new `gslnls`

-package provides R bindings to nonlinear least-squares optimization with
the GNU Scientific Library (GSL) using the trust region methods implemented by the `gsl_multifit_nlinear`

module. The `gsl_multifit_nlinear`

module was added in GSL version 2.2 (released in August 2016) and the available nonlinear-least squares routines have been thoroughly tested and are well documented, see (Galassi et al. 2009).

The aim of this post is to put the GSL nonlinear least-squares routines to the test and
benchmark their optimization performance against R’s standard `nls()`

function based on a small selection of test problems taken from the NIST Statistical Reference Datasets (StRD) archive.

## NIST StRD test problems

The NIST StRD Nonlinear Regression archive includes both generated and *real-world* nonlinear least squares problems of varying levels of difficulty. The generated datasets are designed to challenge specific computations. Real-world data include challenging datasets such as the `Thurber`

problem, and more benign datasets such as `Misra1a`

(not tested here). The certified parameter values are *best-available* solutions, obtained using 128-bit precision and confirmed by at least two different algorithms and software packages using analytic derivatives.

The NIST StRD archive orders the regression problems by level of difficulty (lower, moderate and higher). In this post, only the regression problems that are labeled with a *higher* level of difficulty are tested, as these regression models are generally tedious to fit using R’s default `nls()`

function, especially when the chosen starting values are not close to the least-squares solution.

Table 1 provides an overview of all evaluated test problems including regression models, certified parameter values and starting values. Except for `BoxBOD`

, all of the listed datasets can be loaded directly in R with the `NISTnls`

-package available on CRAN^{1}. For the `BoxBOD`

dataset, the data is parsed separately from the corresponding NIST StRD data (.dat) file.

Dataset name | # Observations | # Parameters | Regression model | Certified parameter values | Starting values | Dataset source | Reference |
---|---|---|---|---|---|---|---|

Rat42 | 9 | 3 | \(f(x) = \dfrac{b_1}{1 + \exp(b_2 – b_3 x)}\) | \([72.462, 2.6181, 0.0673]\) | \([100, 1, 0.1]\) | Observed | Ratkowsky (1983) |

MGH09 | 11 | 4 | \(f(x) = \dfrac{b_1(x^2 + b_2 x)}{x^2 + b_3x + b_4}\) | \([0.1928, 0.1913, 0.1231, 0.1361]\) | \([25, 39, 41.5, 39]\) | Generated | Kowalik and Osborne (1978) |

Thurber | 37 | 7 | \(f(x) = \dfrac{b_1 + b_2x + b_3x^2 + b_4x^3}{1 + b_5x + b_6x^2 + b_7x^3}\) | \([1288.14, 1491.08, 583.238, 75.417, 0.9663, 0.3980, 0.0497]\) | \([1000, 1000, 400, 40, 0.7, 0.3, 0.03]\) | Observed | Thurber (1979) |

MGH10 | 16 | 3 | \(f(x) = b_1 \exp \left( \dfrac{b_2}{x + b_3} \right)\) | \([0.00561, 6181.35, 345.224]\) | \([2, 400000, 25000]\) | Generated | Meyer (1970) |

Eckerle4 | 35 | 3 | \(f(x) = \dfrac{b_1}{b_2} \exp\left( -\dfrac{1}{2} \left(\dfrac{x – b_3}{b_2}\right)^2 \right)\) | \([1.5544, 4.0888, 451.541]\) | \([1, 10, 500]\) | Observed | Eckerle (1979) |

Rat43 | 15 | 4 | \(f(x) = \dfrac{b_1}{(1 + \exp(b_2 – b_3x))^{1/b_4}}\) | \([699.642, 5.2771, 0.7596, 1.2792]\) | \([100, 10, 1, 1]\) | Observed | Ratkowsky (1983) |

Bennett5 | 154 | 3 | \(f(x) = b_1(b_2 + x)^{-1/b_3}\) | \([-2523.51, 46.737, 0.9322]\) | \([-2000, 50, 0.8]\) | Observed | Bennett, Swartzendruber, and Brown (1994) |

BoxBOD | 6 | 2 | \(f(x) = b_1(1 – \exp(-b_2 x))\) | \([213.809, 0.5472]\) | \([1, 1]\) | Observed | Box et al. (1978) |

The regression models and certified parameter values are taken from their respective NIST StRD data (.dat) files. For each test problem, the NIST StRD archive provides two or three sets of parameter starting values for the purpose of testing. The starting values listed in Table 1 correspond to the *most difficult* sets of starting values that are generally the furthest away from the target least-squares solution.

The following plots display all observed datasets, with the (unique) predictor variable on the x-axis and the response variable on the y-axis. The overlayed continuous line corresponds to the regression model evaluated at the certified parameter values.

## Algorithms and control parameters

### Trust region methods

Convergence of the nonlinear least-squares routines is tested across a grid of algorithms and pre-selected control parameter choices. For the GSL nonlinear least-squares algorithms, all trust region methods available through the `gsl_nls()`

function in the `gslnls`

-package are evaluated, i.e. the `algorithm`

argument in `gsl_nls()`

takes the following values:

`"lm"`

, Levenberg-Marquadt algorithm`"lmaccel"`

, Levenberg-Marquadt with geodesic acceleration.`"dogleg"`

, Powell’s Dogleg algorithm`"ddogleg"`

, Double dogleg algorithm`"subspace2D"`

, 2D subspace dogleg generalization.

By default, if the `jac`

argument in the `gsl_nls()`

function is left unspecified, the Jacobian matrix is approximated by (forward) finite differences. Analogously, when geodesic acceleration is used and the `fvv`

argument is left unspecified, the second directional derivatives are approximated by (forward) finite differences. In testing the convergence of the GSL routines, the `jac`

argument is always left unspecified. The Levenberg-Marquadt algorithm with geodesic acceleration is evaluated both with the `fvv`

argument unspecified (denoted by `lmaccel`

) *and* with `fvv = TRUE`

in which case the second directional derivatives are calculated using symbolic differentiation (denoted by `lmaccel+fvv`

).

### Scaling method

For the control parameters set with `gsl_nls_control()`

, only the `scale`

and `solver`

parameters are varied, see also `?gsl_nls_control`

. The maximum number of iterations `maxiter`

is increased from the default `maxiter = 50`

to `maxiter = 1e4`

in order to remove the maximum number of iterations as a constraining factor, and the default values are used for all other control parameters available in `gsl_nls_control()`

.

The `scale`

control parameter can take the following values^{2}:

`"more"`

, Moré rescaling. This method makes the problem scale-invariant and has been proven effective on a large class of problems.`"levenberg"`

, Levenberg rescaling. This method has also proven effective on a large class of problems, but is not scale-invariant. It may perform better for problems susceptible to parameter evaporation (parameters going to infinity).`"marquadt"`

, Marquadt rescaling. This method is scale-invariant, but it is generally considered inferior to both the Levenberg and Moré strategies.

### Solver method

The `solver`

control parameter can take on the following values^{3}:

`"qr"`

, QR decomposition of the Jacobian. This method will produce reliable solutions in cases where the Jacobian is rank deficient or near-singular but does require more operations than the Cholesky method.`"cholesky"`

, Cholesky decomposition of the Jacobian. This method is faster than the QR approach, however it is susceptible to numerical instabilities if the Jacobian matrix is rank deficient or near-singular.`"svd"`

, SVD decomposition of the Jacobian. This method will produce the most reliable solutions for ill-conditioned Jacobians but is also the slowest.

### Benchmark algorithms

In order to benchmark the performance of the GSL nonlinear least-squares routines against several common R alternatives, each nonlinear regression model is also fitted using the standard `nls()`

function, as well as the `nlsLM()`

function from the `minpack.lm`

-package.

For the `nls()`

function, all three available algorithms are tested, i.e. the `algorithm`

argument is set to respectively:

`"default"`

, the default Gauss-Newton algorithm`"plinear"`

, Golub-Pereyra algorithm for partially linear least-squares models`"port"`

,`nl2sol`

algorithm from the Port library

The maximum number of iterations is set to `maxiter = 1e4`

and the relative convergence tolerance is set to `tol = sqrt(.Machine$double.eps)`

to mimic the control parameters used for the GSL routines.

For the `nlsLM()`

function, there is only a single algorithm (Levenberg-Marquadt), so no choice needs to be made here. The maximum number of iterations is set to `maxiter = 1e4`

and all other control parameters are kept at their default values.

## Rat42 example

As a worked out example, we display the different NLS calls used to fit the `Rat42`

nonlinear regression model based on `gsl_nls()`

, `nls()`

and `nlsLM()`

. The `Rat42`

model and data are an example of fitting sigmoidal growth curves taken from (Ratkowsky 1983). The response variable is pasture yield, and the predictor variable is growing times.

y | 8.93 | 10.8 | 18.59 | 22.33 | 39.35 | 56.11 | 61.73 | 64.62 | 67.08 |

x | 9.00 | 14.0 | 21.00 | 28.00 | 42.00 | 57.00 | 63.00 | 70.00 | 79.00 |

### GSL model fit

Similar to `nls()`

, a minimal `gsl_nls()`

function call consists of the model `formula`

, the data and a set of starting values. By default, `gsl_nls()`

uses the Levenberg-Marquadt algorithm (`algorithm = "lm"`

) with control parameters `scale = "more"`

and `solver = "qr"`

. The starting values \((b_1 = 100, b_2 = 1, b_3 = 0.1)\) are taken from Table 1.

library(NISTnls) library(gslnls) ## gsl Levenberg-Marquadt (more+qr) rat42_gsl <- gsl_nls( fn = y ~ b1 / (1 + exp(b2 - b3 * x)), ## model data = Ratkowsky2, ## dataset start = c(b1 = 100, b2 = 1, b3 = 0.1) ## starting values ) rat42_gsl #> Nonlinear regression model #> model: y ~ b1/(1 + exp(b2 - b3 * x)) #> data: Ratkowsky2 #> b1 b2 b3 #> 72.46224 2.61808 0.06736 #> residual sum-of-squares: 8.057 #> #> Algorithm: levenberg-marquardt, (scaling: more, solver: qr) #> #> Number of iterations to convergence: 10 #> Achieved convergence tolerance: 4.619e-14

The `gsl_nls()`

function returns an object that inherits from the class `"nls"`

. For this reason, all generic functions available for `"nls"`

-objects are also applicable to objects returned by `gsl_nls()`

. For instance,

## model fit summary summary(rat42_gsl) #> #> Formula: y ~ b1/(1 + exp(b2 - b3 * x)) #> #> Parameters: #> Estimate Std. Error t value Pr(>|t|) #> b1 72.462238 1.734028 41.79 1.26e-08 *** #> b2 2.618077 0.088295 29.65 9.76e-08 *** #> b3 0.067359 0.003447 19.54 1.16e-06 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 1.159 on 6 degrees of freedom #> #> Number of iterations to convergence: 10 #> Achieved convergence tolerance: 4.619e-14 ## profile confidence intervals confint(rat42_gsl) #> 2.5% 97.5% #> b1 68.76566669 77.19014998 #> b2 2.41558255 2.84839910 #> b3 0.05947284 0.07600439

Note that the existing `predict.nls`

method is extended to allow for the calculation of asymptotic confidence and prediction intervals, in addition to prediction of the expected response:

predict(rat42_gsl, interval = "prediction", level = 0.95) #> fit lwr upr #> [1,] 8.548006 5.385407 11.71060 #> [2,] 11.431085 8.235094 14.62708 #> [3,] 16.727705 13.526235 19.92917 #> [4,] 23.532240 20.326258 26.73822 #> [5,] 40.039555 36.612415 43.46669 #> [6,] 55.963267 52.689429 59.23711 #> [7,] 60.546511 57.382803 63.71022 #> [8,] 64.536158 61.311113 67.76120 #> [9,] 67.913137 64.327402 71.49887

### Benchmark NLS fits

As benchmarks to the model fits obtained with `gsl_nls()`

, each test problem is also fitted with calls to `nls()`

and `minpack.lm::nlsLM()`

. For the `Rat42`

dataset, fitting the regression model with `nls()`

using the default Gauss-Newton algorithm (`algorithm = "default"`

) fails to return a valid result:

## nls default nls( formula = y ~ b1 / (1 + exp(b2 - b3 * x)), ## model data = Ratkowsky2, ## dataset start = c(b1 = 100, b2 = 1, b3 = 0.1) ## starting values ) #> Error in nls(formula = y ~ b1/(1 + exp(b2 - b3 * x)), data = Ratkowsky2, : singular gradient

Switching to the Port algorithm (`algorithm = "port"`

), the `nls()`

call does converge to the target least-squares solution:

## nls port nls( formula = y ~ b1 / (1 + exp(b2 - b3 * x)), ## model data = Ratkowsky2, ## dataset start = c(b1 = 100, b2 = 1, b3 = 0.1), ## starting values algorithm = "port" ## algorithm ) #> Nonlinear regression model #> model: y ~ b1/(1 + exp(b2 - b3 * x)) #> data: Ratkowsky2 #> b1 b2 b3 #> 72.46224 2.61808 0.06736 #> residual sum-of-squares: 8.057 #> #> Algorithm "port", convergence message: relative convergence (4)

And the same is true when using `nlsLM()`

with the default Levenberg-Marquadt algorithm:

## nls LM minpack.lm::nlsLM( formula = y ~ b1 / (1 + exp(b2 - b3 * x)), ## model data = Ratkowsky2, ## dataset start = c(b1 = 100, b2 = 1, b3 = 0.1), ## starting values ) #> Nonlinear regression model #> model: y ~ b1/(1 + exp(b2 - b3 * x)) #> data: Ratkowsky2 #> b1 b2 b3 #> 72.46223 2.61808 0.06736 #> residual sum-of-squares: 8.057 #> #> Number of iterations to convergence: 8 #> Achieved convergence tolerance: 1.49e-08

The `Rat42`

model is *partially linear* in the sense that `y ~ b1 * z`

with `z = 1 / (1 + exp(b2 - b3 * x))`

, which means that the Golub-Pereyra algorithm (`algorithm = "plinear"`

) can also be applied in this example. Note that the model formula is updated to exclude the linear parameter `b1`

, and a starting value for this parameter is no longer required.

## nls plinear nls( formula = y ~ 1 / (1 + exp(b2 - b3 * x)), ## model data = Ratkowsky2, ## dataset start = c(b2 = 1, b3 = 0.1), ## starting values algorithm = "plinear" ## algorithm ) #> Nonlinear regression model #> model: y ~ 1/(1 + exp(b2 - b3 * x)) #> data: Ratkowsky2 #> b2 b3 .lin #> 2.61808 0.06736 72.46224 #> residual sum-of-squares: 8.057 #> #> Number of iterations to convergence: 9 #> Achieved convergence tolerance: 1.119e-06

The p-linear algorithm also converges successfully, with the `b1`

parameter now labeled as `.lin`

(for *linear* parameter) in the fitted model coefficients.

# Model fit results

## Model fit convergence

Below, the convergence status of the evaluated GSL and benchmark NLS routines is displayed for each individual test problem. The obtained convergence results are categorized according to the following status codes:

**success**; the NLS routine converged successfully and the fitted parameters*approximately*coincide with the NIST StRD certified values^{4}.**false convergence**; the NLS routine converged successfully, but the fitted parameters do not coincide with the NIST StRD certified values.**non-zero exit**; the NLS routine failed to converge and returns a valid NLS object with a non-zero exit code.**failed**; the NLS routine failed to converge and returns an error.

Based on the displayed results, an initial observation is that the default Gauss-Newton algorithm in `nls()`

fails to produce *any* successful model fit and returns an error for each selected test problem. The Port and (`minpack.lm`

) Levenberg-Marquadt algorithms show roughly similar convergence results, but only successfully converge for half of the evaluated test problems. The p-linear algorithm is somewhat special as it is only applicable for regression models that can be factored into a partially linear model. However, if applicable, the p-linear algorithm can be a powerful alternative as demonstrated by the `BoxBOD`

problem, where most other (general) NLS routines fail to converge. More precisely, the `BoxBOD`

regression model contains only two parameters, and by factoring out the linear parameter, the nonlinear model fit that needs to be optimized by the p-linear algorithm depends only on a single unknown parameter.

Regarding the GSL routines, for each test problem there exists *at least* one algorithm configuration that is able to produce a successful model fit. Across test problems and control parameter configurations, the GSL Levenberg-Marquadt algorithms with and without geodesic acceleration (`lm`

, `lmaccel`

, `lmaccel+fvv`

) appear to be the most stable, as also seen in the figure below, which displays the total number of successful model fits across test problems. In comparison to the LM algorithm without geodesic acceleration (`lm`

), the LM algorithm with geodesic acceleration (`lmaccel`

, `lmaccel+fvv`

) fails to converge in the `Rat43`

problem, but on the other hand the `lmaccel+fvv`

algorithm is the *only* GSL routine that is able to produce a successful fit in the `BoxBOD`

problem.

Across control parameter configurations, in terms of the scaling method, `more`

rescaling (the default) exhibits the most stable performance, followed by `marqaudt`

rescaling and `levenberg`

rescaling. In the figure below, this is seen most prominently for the different variations of the Dogleg algorithm (`dogleg`

, `ddogleg`

, `subspace2D`

) and somewhat less for the Levenberg-Marquadt algorithms. In terms of the chosen solver method, the `svd`

solver produces slightly more robust results than the `qr`

and `cholesky`

solver methods, which is in line with the solver method section in the GSL documentation.

## Iterations to convergence

As supplementary information, we also display the required number of iterations to reach convergence for each successfully converged NLS routine. In case of a successful model fit, the Port algorithm requires only a small number of iterations to reach convergence. The number of iterations required by the `minpack.lm`

Levenberg-Marquadt algorithm and GSL Levenberg-Marquadt algorithm(s) is of the same order of magnitude. Among the GSL routines, except for the `MGH09`

problem, the general tendency is that the Dogleg-based algorithms (`dogleg`

, `ddogleg`

, `subspace2D`

) require less iterations than the LM-based algorithms. This is illustrated most clearly by the `Rat42`

and `Bennet5`

plots.

# Conclusion

Based on a small collection of NIST StRD test problems, this post benchmarks the convergence properties of a number of GSL nonlinear least squares routines as well as several standard NLS algorithms that are in common use. For the tested nonlinear regression problems, the GSL algorithms show at least comparable –*and often better*– optimization performance than the included benchmark algorithms, using mostly standard choices and default values for the GSL trust region method control parameters. As such, the GSL trust region methods provide a useful supplement to the existing suite of nonlinear least squares fitting algorithms available in R, in particular when adequate starting values are difficult to come by and more stable optimization routines (than provided by R’s standard methods) are required.

# References

*National Institute of Standards and Technology (NIST), US Department of Commerce, USA*.

*Statistics for Experimenters*. New York: Wiley.

*National Institute of Standards and Technology (NIST), US Department of Commerce, USA*13.

*GNU Scientific Library Reference Manual*. 3rd ed. Network Theory Limited. https://www.gnu.org/software/gsl/.

*Methods for Unconstrained Optimization Problems*. New York: Elsevier.

*Nonlinear Programming*, 465–86. New York: Elsevier.

*ACM Transactions on Mathematical Software (TOMS)*7 (1): 17–41.

*Nonlinear Regression Modelling*. New York: Marcel Dekker.

*National Institute of Standards and Technology (NIST), US Department of Commerce, USA*.

https://cran.r-project.org/web/packages/NISTnls/index.html↩︎

https://www.gnu.org/software/gsl/doc/html/nls.html#c.gsl_multifit_nlinear_scale↩︎

https://www.gnu.org/software/gsl/doc/html/nls.html#c.gsl_multifit_nlinear_solver↩︎

Here, the maximum relative deviation of the fitted values with respect to the certified values is within a small tolerance range \(\epsilon\).↩︎

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