optim_doc: bfgsmin

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### 1.13 Unconstrained BFGS algorithm

BFGS or limited memory BFGS minimization of a function. No constraits are honoured.

#### Helptext:

    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

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