# 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, ...)`

##### 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
can be used to find an optimal board, by generating random permissible
boards and finding the best one. 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

##### Examples

```
a <- matrix(0,5,5)
diag(a) <- NA
a[cbind(1:5 , c(2:5,1))] <- 4
## Not run:
# best(a,control=list(maxit=10)) ## Answer should be all ones except the diagonal
# ## End(Not run)
# Now a non-default function; SANN should be able to get func(x) down to
# zero pretty quickly:
## Not run:
# best(a,func=function(x){x[1,2]},control=list(maxit=100))
# ## End(Not run)
# The 'dontrun' is needed because sometimes the method needs a bigger n
```

*Documentation reproduced from package aylmer, version 1.0-11, License: GPL-2*