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.
You can install the released package version from CRAN with:
install.packages("trackopt")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()
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