Ben Bolker <bbolker <at> gmail.com> writes:
>
> Simulated annealing and other stochastic global optimization
> methods are also possible solutions, although they may or may not
> work better than the many-starting-points solution -- it depends
> on the problem, and pretty much everything has to be tuned. Tabu
> search <http://en.wikipedia.org/wiki/Tabu_search> is another possibility,
> although I don't know much about it ...
>
It is known that the Excel Solver has much improved during recent years.
Still there are slightly better points such as
myfunc(c(0.889764228112319, 94701144.5712312)) # 334.18844
restricting the domain to [0, 1] x [0, 10^9] for an evolutionary approach,
for instance DEoptim::DEoptim().
Finding a global optimum in 2 dimensions is not so difficult. Here the scale
of the second variable could pose a problem as small local minima might be
overlooked easily.
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.
