Hi Dmitrey, 2011/8/15 Dmitrey <tm...@ukr.net>: > Hi all, > I'm glad to inform you that general constraints handling for interalg (free > solver with guaranteed user-defined precision) now is available. Despite it > is very premature and requires lots of improvements, it is already capable > of outperforming commercial BARON (example: > http://openopt.org/interalg_bench#Test_4) and thus you could be interested > in trying it right now (next OpenOpt release will be no sooner than 1 > month). > > interalg can be especially more effective than BARON (and some other > competitors) on problems with huge or absent Lipschitz constant, for example > on funcs like sqrt(x), log(x), 1/x, x**alpha, alpha<1, when domain of x is > something like [small_positive_value, another_value]. > > Let me also remember you that interalg can search for all solutions of > nonlinear equations / systems of them where local solvers like > scipy.optimize fsolve cannot find anyone, and search single/multiple > integral with guaranteed user-defined precision (speed of integration is > intended to be enhanced in future). > However, only FuncDesigner models are handled (read interalg webpage for > more details).
Thank you for this new improvements. I am one of those who use OpenOpt in real life problems, and if I can advance a suggestion (for the second time), when you post a benchmark of various optimization methods, please do not consider the "elapsed time" only as a meaningful variable to measure a success/failure of an algorithm. Some (most?) of real life problems require intensive and time consuming simulations for every *function evaluation*; the time spent by the solver itself doing its calculations simply disappears in front of the real process simulation. I know it because our simulations take between 2 and 48 hours to run, so what's 300 seconds more or less in the solver calculations? If you talk about synthetic problems (such as the ones defined by a formula), I can see your point. For everything else, I believe the number of function evaluations is a more direct way to assess the quality of an optimization algorithm. Just my 2c. Andrea. "Imagination Is The Only Weapon In The War Against Reality." http://xoomer.alice.it/infinity77/ >>> import PyQt4.QtGui Traceback (most recent call last): File "<interactive input>", line 1, in <module> ImportError: No module named PyQt4.QtGui >>> >>> import pygtk Traceback (most recent call last): File "<interactive input>", line 1, in <module> ImportError: No module named pygtk >>> >>> import wx >>> >>> _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion