[Octave] fminunc()

fminunc( FCN, X0);

fminunc( FCN, C0, Options);

[X, FVEC, INFO, OUTPUT, GRAD, HESS] = fminunc (FCN, ...);

%Solve an unconstrained optimization problem defined by the function FCN.

%X0 determines a starting guess.

%OPTIONS is a structure specifying additional options.  Currently, `fminunc‘ recognizes these %options: "FunValCheck", "OutputFcn", "TolX", "TolFun", "MaxIter", "MaxFunEvals", "GradObj", %"FinDiffType", "TypicalX", "AutoScaling".  (optimset)

%On return, FVAL contains the value of the function FCN evaluated at X

%INFO may be one of the following values:
%1 Converged to a solution point.
%2 Last relative step size was less that TolX.
%3 Last relative decrease in function value was less than TolF.
%0 Iteration limit exceeded.
%-3 The trust region radius became excessively small.

%Notes: If you only have a single nonlinear equation of one
%     variable then using `fminbnd‘ is usually a much better idea.  The
%     algorithm used is a gradient search which depends on the objective
%     function being differentiable.  If the function has
%     discontinuities it may be better to use a derivative-free
%     algorithm such as `fminsearch‘.