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[nlopt.git] / api / nlopt_minimize.3

diff --git a/api/nlopt_minimize.3 b/api/nlopt_minimize.3
index b257003698ac760148036f59b1a6a048471a326b..6c1c10863606fd7b0d6702d8e8f91689d03ce6cd 100644 (file)
--- a/api/nlopt_minimize.3
+++ b/api/nlopt_minimize.3
@@ -204,15 +204,15 @@ DIRECT-L search algorithm by Jones et al. as modified by Gablonsky et
 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).
@@ -241,20 +241,28 @@ algorithms are only available if NLopt is compiled with C++ enabled,
 and should be linked via -lnlopt_cxx (via a C++ compiler, in order
 to link the C++ standard libraries).
 .TP 
-.B NLOPT_LN_SUBPLEX
+.B NLOPT_LN_NELDERMEAD
 Perform a local (L) derivative-free (N) optimization, starting at
 .IR x ,
-using the Subplex algorithm of Rowan et al., which is an improved
-variant of Nelder-Mead simplex algorithm.  (Like Nelder-Mead, Subplex
-often works well in practice, even for discontinuous objectives, but
-there is no rigorous guarantee that it will converge.)  Subplex is
-best for unconstrained optimization, but constrained optimization also
-works (both for simple bound constraints via
-.I lb
-and
-.I ub
-as well as nonlinear constraints via the crude technique of returning
-+Inf when the constraints are violated, as explained above).
+using the Nelder-Mead simplex algorithm, modified to support bound
+constraints.  Nelder-Mead, while popular, is known to occasionally
+fail to converge for some objective functions, so it should be
+used with caution.  Anecdotal evidence, on the other hand, suggests
+that it works fairly well for discontinuous objectives.  See also
+.B NLOPT_LN_SBPLX
+below.
+.TP 
+.B NLOPT_LN_SBPLX
+Perform a local (L) derivative-free (N) optimization, starting at
+.IR x ,
+using an algorithm based on the Subplex algorithm of Rowan et al.,
+which is an improved variant of Nelder-Mead (above).  Our
+implementation does not use Rowan's original code, and has some minor
+modifications such as explicit support for bound constraints.  (Like
+Nelder-Mead, Subplex often works well in practice, even for
+discontinuous objectives, but there is no rigorous guarantee that it
+will converge.)  Nonlinear constraints can be crudely supported
+by returning +Inf when the constraints are violated, as explained above.
 .TP
 .B NLOPT_LN_PRAXIS
 Local (L) derivative-free (N) optimization using the principal-axis
@@ -478,10 +486,6 @@ calling:
 Some of the algorithms also support using low-discrepancy sequences (LDS),
 sometimes known as quasi-random numbers.  NLopt uses the Sobol LDS, which
 is implemented for up to 1111 dimensions.
-.SH BUGS
-Currently the NLopt library is in pre-alpha stage.  Most algorithms
-currently do not support all termination conditions: the only
-termination condition that is consistently supported right now is
 .BR maxeval .
 .SH AUTHORS
 Written by Steven G. Johnson.