On Tue, Nov 23, 2010 at 11:17 AM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: > On Tue, Nov 23, 2010 at 11:13:23AM +0100, Sebastian Walter wrote: >> I'm not familiar with dichotomy optimization. >> Several techniques have been proposed to solve the problem: genetic >> algorithms, simulated annealing, Nelder-Mead and Powell. >> To be honest, I find it quite confusing that these algorithms are >> named in the same breath. > > I am confused too. But that stems from my lack of knowledge in > optimization. > >> Do you have a continuous or a discrete problem? > > Both. > >> Is your problem of the following form? > >> min_x f(x) >> s.t. lo <= Ax + b <= up >> 0 = g(x) >> 0 <= h(x) > > No constraints.
didn't you say that you operate only in some convex hull? > >> An if yes, in which space does x live? > > Either in R^n, in the set of integers (unidimensional), or in the set of > positive integers. According to http://openopt.org/Problems this is a mixed integer nonlinear program http://openopt.org/MINLP . I don't have experience with the solver though, but it may take a long time to run it since it uses branch-and-bound. In my field of work we typically relax the integers to real numbers, perform the optimization and then round to the next integer. This is often sufficiently close a good solution. > > Gaël > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion