bfgsmin: bfgs or limited memory bfgs minimization of function

 Usage: [x, obj_value, convergence, iters] = bfgsmin(f, args, control)

 The function must be of the form
 [value, return_2,..., return_m] = f(arg_1, arg_2,..., arg_n)
 By default, minimization is w.r.t. arg_1, but it can be done
 w.r.t. any argument that is a vector. Numeric derivatives are
 used unless analytic derivatives are supplied. See bfgsmin_example.m
 for methods.

 Arguments:
 * f: name of function to minimize (string)
 * args: a cell array that holds all arguments of the function
 	The argument with respect to which minimization is done
 	MUST be a vector
 * control: an optional cell array of 1-8 elements. If a cell
   array shorter than 8 elements is provided, the trailing elements
   are provided with default values.
 	* elem 1: maximum iterations  (positive integer, or -1 or Inf for unlimited (default))
 	* elem 2: verbosity
 		0 = no screen output (default)
 		1 = only final results
 		2 = summary every iteration
 		3 = detailed information
 	* elem 3: convergence criterion
 		1 = strict (function, gradient and param change) (default)
 		0 = weak - only function convergence required
 	* elem 4: arg in f_args with respect to which minimization is done (default is first)
 	* elem 5: (optional) Memory limit for lbfgs. If it's a positive integer
 		then lbfgs will be use. Otherwise ordinary bfgs is used
 	* elem 6: function change tolerance, default 1e-12
 	* elem 7: parameter change tolerance, default 1e-6
 	* elem 8: gradient tolerance, default 1e-5

 Returns:
 * x: the minimizer
 * obj_value: the value of f() at x
 * convergence: 1 if normal conv, other values if not
 * iters: number of iterations performed

 Example: see bfgsmin_example.m

Package: optim