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<h3 class="section">1.13 Unconstrained BFGS algorithm</h3>
<p><a name="index-bfgsmin-18"></a>
BFGS or limited memory BFGS minimization of a function. No constraits
are honoured.
<h4 class="subheading">Helptext:</h4>
<p><a name="XREFbfgsmin"></a>
<pre class="verbatim">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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