best

Optimize a board using simulated annealing

Uses simulated annealing to find the `best' permissible board, using any objective function

Usage

best(x, func = NULL, n = 100, ...)

Details

The help page for optim() gives an example of simulated annealing being used to solve the travelling salesman problem and best() uses the same technique in which the gr argument specifies a function used to generate a new candidate point (candidate()).

Note

Function randomprobs() also takes a func argument and (if argument give.best is TRUE) will return the optimal board. But these two functions are very different: best() uses optim() which incorporates highly specific optimization algorithms to find a global maximum, while randomprobs() creates a Markov chain and reports the board with the most desirable objective function.

See Also

optim,prob

Aliases

• best

Examples

a <- matrix(0,5,5)
 diag(a) <- NA
 a[cbind(1:5 , c(2:5,1))] <- 4
 best(a,control=list(maxit=10))   ## Answer should be all ones except the diagonal


# Now a non-default function; SANN should be able to get func(x) down to
#  zero pretty quickly:

 best(a,func=function(x){x[1,2]},control=list(maxit=100))