Hi Ravi! Ravi Varadhan wrote: > > Yes. Most classical optimization methods (e.g. gradient-type, > Newton-type) are "local", i.e. they do not attempt to locate the > global optimum.
Ah .. I see. > The primary difficulty with global optimization is that there are no > mathematical conditions that characterize global optimum in > multi-modal problems. <..> > A simplistic strategy to find global optimum is to use local > methods with multiple starting values. :-) .. well, that is somewhat similar to the approach the genetic algorithm uses, well, at least with respect of having many starting points. > Again the problem is that you don't have any guarantee that you have > found the global optimum. Well, then it looks like I am out of luck if I wanted to plug in a function and provide end ranges and get the global max/min. I was hoping this would provide a way for me to verify the workings of a genetic algorithm I am testing. I appreciate you taking the time to explain this so clearly, thanks again, Esmail ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
