The help file points out that CG is "fragile" ... and I would expect that
failing to define a gradient function will exacerbate that.
I think you should use a different algorithm or specify a gradient function.
You might also consider working with the more recent optimr package contributed
by Dr Nash, author of the original optim function in R.
On March 12, 2020 2:30:26 AM PDT, Skyler Saleebyan <skylerbsaleeb...@gmail.com>
wrote:
>I am trying to familiarize myself with optim() with a relatively simple
>maximization.
>
>Description:
>L and K are two terms which are constrained to add up to a total 100000
>(with respective weights to each). To map this constraint I plugged K
>into
>the function (to make this as simple as possible.)
>
>Together these two feed into one nonlinear function which is the
>product of
>two monotonic (on the positive interval) functions. Then that numbers
>is
>returned in a function fed to optim, which should maximize the output
>by
>adjusting L. The whole code is:
>
>production1 <- function(L){
> budget=100000
> Lcost=12
> Kcost=15
> K=(budget-L*Lcost)/Kcost
> machines=0.05*L^(2/3)*K^(1/3)
> return(machines)
>}
>
># production1(6000) #example of number with much higher output vs optim
>result
>S1=optim(1001,production1,method="CG",control=list(fnscale=-1))
>S1
>
>Output:
>$par
>[1] 1006.536
>
>$value
>[1] 90.54671
>
>$counts
>function gradient
> 201 101
>
>$convergence
>[1] 1
>
>$message
>NULL
>
>
>For some reason this never explores the problem space and just spits
>out
>some answer close to the initial condition. What am I doing wrong?
>
>Thanks,
>Skyler S.
>
> [[alternative HTML version deleted]]
>
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--
Sent from my phone. Please excuse my brevity.
______________________________________________
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