# best

0th

Percentile

##### 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, ...)
##### Arguments
x
A board
func
The objective function, with default NULL meaning to use -prob(x)
n
Maximum number of attempts (passed to candidate())
...
Further arguments passed to optim()
##### 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.

optim,prob

• 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))`
Documentation reproduced from package aylmer, version 1.0-1, License: GPL-2

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