al. to be more weighted towards local search. Does not support
unconstrainted optimization. There are also several other variants of
the DIRECT algorithm that are supported:
-.BR NLOPT_GLOBAL_DIRECT ,
+.BR NLOPT_GN_DIRECT ,
which is the original DIRECT algorithm;
-.BR NLOPT_GLOBAL_DIRECT_L_RAND ,
+.BR NLOPT_GN_DIRECT_L_RAND ,
a slightly randomized version of DIRECT-L that may be better in
high-dimensional search spaces;
-.BR NLOPT_GLOBAL_DIRECT_NOSCAL ,
-.BR NLOPT_GLOBAL_DIRECT_L_NOSCAL ,
+.BR NLOPT_GN_DIRECT_NOSCAL ,
+.BR NLOPT_GN_DIRECT_L_NOSCAL ,
and
-.BR NLOPT_GLOBAL_DIRECT_L_RAND_NOSCAL ,
+.BR NLOPT_GN_DIRECT_L_RAND_NOSCAL ,
which are versions of DIRECT where the dimensions are not rescaled to
a unit hypercube (which means that dimensions with larger bounds are
given more weight).
.SH AUTHORS
Written by Steven G. Johnson.
.PP
-Copyright (c) 2007-2008 Massachusetts Institute of Technology.
+Copyright (c) 2007-2014 Massachusetts Institute of Technology.
.SH "SEE ALSO"
nlopt_minimize(3)