Paul Smith <phhs80 <at> gmail.com> writes: > The problem with DEoptim approach is that is not guaranteed that it > converges to the solution. Moreover, from my experience, it seems to > be quite slow when the optimization problem is high-dimensional (i.e., > with many variables). > > Paul
There is a difference between local and global optimization: 'optim' realizes *local* optimization using a gradient-based approach. This is fast, but will get stuck in local optima (except method SANN). 'DEoptim' is one of many approaches to *global* optimization, of which each has its advantages and drawbacks. > ...not guaranteed that it converges to the solution. As a local optimization routine, also 'optim' does not guarantee to reach a (global) optimum. > [DEoptim] seems to be quite slow... This is normal for routines in global optimization as they have to search a quite large space. Hans Werner ______________________________________________ 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.
