Hi Steven, I am using NLopt now and I am quite happy with the results.
Thank you! On Sunday, January 19, 2014 4:29:00 PM UTC+1, Steven G. Johnson wrote: > > The NLopt package provides both gradient-based (where you have to supply > the analytical gradient) and derivative-free (where you only supply the > objective function) optimizers. > > It is not really practical to do optimization of "very" high dimensional > problems without knowing the gradient analytically. (Normally you > shouldn't need to provide a Hessian, however.) But I think of "very" as > being 1000s of dimensions; if you only have tens of dimensions, that is > fine for derivative-free optimizers. > > On Thursday, January 16, 2014 7:03:27 PM UTC-5, jbeginner wrote: > >> I am trying to use Julia's Ipopt interface for an optimization problem. I >> have two questions. Firstly, is it possible to only provide the objective >> function and starting values and not bother about the gradient, hessian, >> etc, or alternatively would providing the objective function and gradient >> suffice? I know that this greatly reduces the performance of the solver but >> is it possible? My function is very high dimensional and it would be very >> cumbersome to compute those manually. >> >
