I have looked in the reference
Schnabel, R. B., Koontz, J. E. and Weiss, B. E. (1985) A modular
system of algorithms for unconstrained minimization. _ACM Trans.
Math. Software_, *11*, 419-440.
cited in the nlm help.This article says that the algorithm permits the use of step selection (line search, dogleg and optimal step), analytic or finite diference gradient and analytic, finite diference or BFGS Hessian aproximation.
Looking back in the nlm help, it has the information that:
a) it does just the line search step selecion;
b) it has the option to inform the gradient and the Hessian by attributes if the user wants.
My questions are:
1) When I do not supply the Hessian, the function does finite difference or BFGS approximation? (Is it possible to select one or other?)
2) I have already used the option to inform the gradient but I don't know how to inform the Hessian. Anybody has an example?
3) I have never heard of this step selections (line search, dogleg and optimal step). I would like to know something about it. I would appreciate if someone could send references for me to learn the subject.
Sincerely,
-- Frederico Zanqueta Poleto [EMAIL PROTECTED] -- "An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem." J. W. Tukey
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