[SciPy-user] [OpenOpt] evaluation of f(x) and df(x)

[Emanuele Olivetti]

Dear All and Dmitrey,

in my code the evaluation of f(x) and df(x) shares many
intermediate steps. I'd like to re-use what is computed
inside f(x) to evaluate df(x) more efficiently, during f(x)
optimization. Then is it _always_ true that, when OpenOpt
evaluates df(x) at a certain x=x^*, f(x) too was previously
evaluated at x=x^*? And in case f(x) was evaluated multiple
times before evaluating df(x), is it true that the last x at
which f(x) was evaluated (before computing df(x=x^*))
was x=x^*?

If these assumptions holds (as it seems from preliminary
tests on NLP using ralg), the extra code to take advantage
of this fact is extremely simple.

Best,

Emanuele

P.S.: if the previous assumptions are false in general, I'd
like to know it they are true at least for the NLP case.