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1.18 A simulated annealing (stochastic) optimizer

This simulated annealing algorithm implicitely honours bound constraints.

Helptext:

samin: simulated annealing minimization of a function. See samin_example.m

usage: [x, obj, convergence, details] = samin(\"f\", {args}, {control})

Arguments:
* \"f\": function name (string)
* {args}: a cell array that holds all arguments of the function,
* {control}: a cell array with 11 elements
	* LB  - vector of lower bounds
	* UB - vector of upper bounds
	* nt - integer: # of iterations between temperature reductions
	* ns - integer: # of iterations between bounds adjustments
	* rt - (0 < rt <1): temperature reduction factor
	* maxevals - integer: limit on function evaluations
	* neps - integer:  number of values final result is compared to
	* functol -   (> 0): the required tolerance level for function value
	                   comparisons
	* paramtol -  (> 0): the required tolerance level for parameters
	* verbosity - scalar: 0, 1, or 2.
		* 0 = no screen output
		* 1 = only final results to screen
		* 2 = summary every temperature change
	* minarg - integer: which of function args is minimization over?

Returns:
* x: the minimizer
* obj: the value of f() at x
* convergence:
	0 if no convergence within maxevals function evaluations
	1 if normal convergence to a point interior to the parameter space
	2 if convergence to point very near bounds of parameter space
	  (suggest re-running with looser bounds)
* details: a px3 matrix. p is the number of times improvements were found.
           The columns record information at the time an improvement was found
           * first: cumulative number of function evaluations
           * second: temperature
           * third: function value

Example: see samin_example