From: Julien Schueller <schueller@phimeca.com>
Date: Tue, 12 Jul 2016 08:20:52 +0000 (+0200)
Subject: Fix _NOSCAL algo typos in the man (#70)
X-Git-Url: http://www.chiark.greenend.org.uk/ucgi/~ianmdlvl/git?a=commitdiff_plain;h=4ef0e556d0f005b41aaf9e223cdfa66da87a00d7;p=nlopt.git

Fix _NOSCAL algo typos in the man (#70)
---

diff --git a/api/nlopt.3 b/api/nlopt.3
index 3234093..709d48b 100644
--- a/api/nlopt.3
+++ b/api/nlopt.3
@@ -316,15 +316,15 @@ 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).
diff --git a/api/nlopt_minimize.3 b/api/nlopt_minimize.3
index 1d30d0f..6c1c108 100644
--- 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).
diff --git a/api/nlopt_minimize_constrained.3 b/api/nlopt_minimize_constrained.3
index b4c9f22..09eb205 100644
--- a/api/nlopt_minimize_constrained.3
+++ b/api/nlopt_minimize_constrained.3
@@ -306,15 +306,15 @@ 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).
diff --git a/direct/README b/direct/README
index 785fb03..4946412 100644
--- a/direct/README
+++ b/direct/README
@@ -21,8 +21,8 @@ algorithm, as described in:
 	of the DIRECT algorithm," J. Global Optimization 21 (1),
 	p. 27-37 (2001).
 
-Both the original Jones algorithm (NLOPT_GLOBAL_DIRECT) and the
-Gablonsky modified version (NLOPT_GLOBAL_DIRECT_L) are implemented
+Both the original Jones algorithm (NLOPT_GN_DIRECT) and the
+Gablonsky modified version (NLOPT_GN_DIRECT_L) are implemented
 and available from the NLopt interface.  The Gablonsky version
 makes the algorithm "more biased towards local search" so that it
 is more efficient for functions without too many local minima.