Question
In R, is it possible to find the Jacobian/Hessian/sparsity pattern analytically when you provide just the objective function and constraints for an optimization problem?
AMPL does this, and from what I hear even MATLAB can do this, but I don't know if you need Knitro for this.
However, all the optimization tools for R (such as nloptr) seem to require me to enter the gradient and Hessian myself, which is very difficult since I am working with a complex model.
Solution
What you are looking for is called automatic differentiation. Sadly, it looks like to me it is not available in R.
There were attempts 5 years ago to implement it but my short investigation indicates that these attempts died out.
There is a fairly recent R package (Automatic Differentiation Model Builder) but it is unclear to me how to use it, or how to apply this to your situation. (I don't use R myself, that's why I don't know.)
OTHER TIPS
1) The default Nelder Mead method in optim does not need derivatives and does not compute them internally either.
2) D, deriv and related R functions (see ?deriv) can compute simple symbolic derivatives.
3) The Ryacas package can compute symbolic derivatives.
I wrote a basic package for Automatic Differentation in R, called madness. While it is primarily designed for the multivariate delta method, it should also be usable for computing gradients automatically. An example usage:
https://stackoverflow.com/questions/21926045
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Russianrequire(madness)
# the 'fit' is the Frobenius norm of Y - L*R
# with a penalty for strongly negative R.
compute_fit <- function(R,L,Y) {
Rmad <- madness(R)
Err <- Y - L %*% Rmad
penalty <- sum(exp(-0.1 * Rmad))
fit <- norm(Err,'f') + penalty
}
set.seed(1234)
R <- array(runif(5*20),dim=c(5,20))
L <- array(runif(1000*5),dim=c(1000,5))
Y <- array(runif(1000*20),dim=c(1000,20))
ftv <- compute_fit(R,L,Y)
show(ftv)
show(val(ftv))
show(dvdx(ftv))
I get back the following:
class: madness
d (norm((numeric - (numeric %*% R)), 'f') + (sum(exp((numeric * R)), na.rm=FALSE) + numeric))
calc: ------------------------------------------------------------------------------------------------
d R
val: 207.6 ...
dvdx: 4.214 4.293 4.493 4.422 4.672 2.899 2.222 2.565 2.854 2.718 4.499 4.055 4.161 4.473 4.069 2.467 1.918 2.008 1.874 1.942 0.2713 0.2199 0.135 0.03017 0.1877 5.442 4.81
1 5.472 5.251 4.674 1.933 1.62 1.79 1.902 1.665 5.232 4.435 4.789 5.183 5.084 3.602 3.477 3.419 3.592 3.376 4.109 3.937 4.017 3.816 4.2 1.776 1.784 2.17 1.975 1.699 4.365 4
.09 4.475 3.964 4.506 1.745 1.042 1.349 1.084 1.237 3.1 2.575 2.887 2.524 2.902 2.055 2.441 1.959 2.467 1.985 2.494 2.223 2.124 2.275 2.546 3.497 2.961 2.897 3.111 2.9 4.44
2 3.752 3.939 3.694 4.326 0.9582 1.4 0.8971 0.8756 0.9019 2.399 2.084 2.005 2.154 2.491 ...
varx: ...
[,1]
[1,] 207.6
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27]
[1,] 4.214 4.293 4.493 4.422 4.672 2.899 2.222 2.565 2.854 2.718 4.499 4.055 4.161 4.473 4.069 2.467 1.918 2.008 1.874 1.942 0.2713 0.2199 0.135 0.03017 0.1877 5.442 4.811
[,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54]
[1,] 5.472 5.251 4.674 1.933 1.62 1.79 1.902 1.665 5.232 4.435 4.789 5.183 5.084 3.602 3.477 3.419 3.592 3.376 4.109 3.937 4.017 3.816 4.2 1.776 1.784 2.17 1.975
[,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81]
[1,] 1.699 4.365 4.09 4.475 3.964 4.506 1.745 1.042 1.349 1.084 1.237 3.1 2.575 2.887 2.524 2.902 2.055 2.441 1.959 2.467 1.985 2.494 2.223 2.124 2.275 2.546 3.497
[,82] [,83] [,84] [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] [,99] [,100]
[1,] 2.961 2.897 3.111 2.9 4.442 3.752 3.939 3.694 4.326 0.9582 1.4 0.8971 0.8756 0.9019 2.399 2.084 2.005 2.154 2.491
Note that the derivative is the derivative of the scalar with respect to a 5 x 20 matrix, but is flattened here to a vector. (Unfortunately a row vector.)
Take a look at solnp, package Rsolnp. It is a nonlinear programming solver which does not require analytical Jacobian or Hessian:
min f(x)
s.t. g(x) = 0
l[h] <= h(x) <= u[h]
l[x] <= x <= u[x]
