Track Numerical Optimization.

Track numerical optimization

The {trackopt} package tracks parameter values, gradients, and Hessians at each iteration of numerical optimizers in R. This can be useful for analyzing optimization progress, diagnosing issues, and studying convergence behavior.

Installation

You can install the released package version from CRAN with:

install.packages("trackopt")

Example

The following is the nlm minimization track of the Himmelblau’s function:

library("trackopt")
himmelblau <- function(x) (x[1]^2 + x[2] - 11)^2 + (x[1] + x[2]^2 - 7)^2
track <- nlm_track(f = himmelblau, p = c(0, 0))
print(track)
#> # A tibble: 17 × 7
#>    iteration         value     step parameter gradient  hessian        seconds
#>  *     <dbl>         <dbl>    <dbl> <list>    <list>    <list>           <dbl>
#>  1         0 170            0       <dbl [2]> <dbl [1]> <dbl [1]>     0       
#>  2         1  47.4         -1.23e+2 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.0247  
#>  3         2  14.0         -3.34e+1 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00119 
#>  4         3   4.91        -9.08e+0 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00122 
#>  5         4   2.26        -2.65e+0 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00127 
#>  6         5   0.951       -1.31e+0 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00110 
#>  7         6   0.272       -6.79e-1 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00102 
#>  8         7   0.0650      -2.07e-1 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.000993
#>  9         8   0.0168      -4.82e-2 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.000989
#> 10         9   0.00400     -1.28e-2 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.000997
#> 11        10   0.000948    -3.06e-3 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.000997
#> 12        11   0.000221    -7.28e-4 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00100 
#> 13        12   0.0000512   -1.69e-4 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00107 
#> 14        13   0.0000118   -3.94e-5 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00100 
#> 15        14   0.00000275  -9.05e-6 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00101 
#> 16        15   0.000000628 -2.13e-6 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00166 
#> 17        16   0.000000152 -4.76e-7 <dbl [2]> <dbl [2]> <dbl [2 × 2]> 0.00102
summary(track)
#> Iterations: 16
#> Function improvement: 170 -> 1.521e-07
#> Computation time: 0.04125 seconds
#> Initial parameter: 0, 0
#> Final parameter: 3, 2
ggplot2::autoplot(track)

The following is the optim maximization track of the Beta-PDF:

optim_track(
  f = dbeta, p = 0, lower = 0, upper = 1, shape1 = 4, shape2 = 2, method = "Brent", minimize = FALSE
) |> ggplot2::autoplot()

Contact

If you have any questions, found a bug, need a feature, just file an issue on GitHub.