# Re: [R] Optimization with constraint.

From: Hans W Borchers <hwborchers_at_gmail.com>
Date: Fri, 14 Mar 2008 18:42:31 +0000 (UTC)

Andreas Klein <klein82517 <at> yahoo.de> writes:
>
> Hello.
>
> I have some problems, when I try to model an
> optimization problem with some constraints.
>
> The original problem cannot be solved analytically, so
> I have to use routines like "Simulated Annealing" or
>
> But to see how all this works in R, I would like to
> basics:
>
> The Problem:
> min f(x1,x2)= (x1)^2 + (x2)^2
> s.t. x1 + x2 = 1
>
> The analytical solution:
> x1 = 0.5
> x2 = 0.5
>
> Does someone have some suggestions how to model it in
> R with the given functions optim or constrOptim with
> respect to the routines "SANN" or "SQP" to obtain the
> analytical solutions numerically?
>

In optimization problems, very often you have to replace an equality by two inequalities, that is you replace x1 + x2 = 1 with

min f(x1,x2)= (x1)^2 + (x2)^2
s.t. x1 + x2 >= 1

x1 + x2 <= 1

The problem with your example is that there is no 'interior' starting point for this formulation while the documentation for constrOptim requests:

The starting value must be in the interior of the feasible region,     but the minimum may be on the boundary.

You can 'relax', e.g., the second inequality with x1 + x2 <= 1.0001 and use (1.00005, 0.0) as starting point, and you will get a solution:

>>> A <- matrix(c(1, 1, -1, -1), 2)
>>> b <- c(1, -1.0001)

>>> fr <- function (x) { x1 <- x; x2 <- x; x1^2 + x2^2 }

>>> constrOptim(c(1.00005, 0.0), fr, NULL, ui=t(A), ci=b)

\$par
 0.5000232 0.4999768
\$value
 0.5
[...]
\$barrier.value
 9.21047e-08

where the accuracy of the solution is certainly not excellent, but the solution is correctly fulfilling x1 + x2 = 1.

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