There seem to be two main kinds of test function for no-derivative optimizers:
- one-liners like the Rosenbrock function ff., with start points
- sets of real data points, with an interpolator
Is it possible to compare say 10d Rosenbrock
with any real 10d problems ?
One could compare in various ways: describe the structure of local minima,
or run optimizers A B C on Rosenbrock and on some real problems;
but both of these seem difficult.
(Maybe theorists and experimenters are just two quite different cultures, so I'm asking for a chimera ?)
- scicomp.SE question: Where can one obtain good data sets/test problems for testing algorithms/routines?
- Hooker, "Testing Heuristics: We Have It All Wrong" is scathing: "the emphasis on competition ... tells us which algorithms are better but not why."
Warning, added in May 2014: there are different Rosenbrock functions:
Dear Prof ...,
so the Rosenbrock in bbobbenchmarks.py is non-standard,
nothing to do with the standard http://en.wikipedia.org/wiki/Rosenbrock_function ?
Of course it has nothing to do with the standard Rosenbrock function, what did you think, why would it?