changed nlopt_minimize_c to nlopt_minimize_constrained, added man page

[nlopt.git] / api / nlopt_minimize.3

diff --git a/api/nlopt_minimize.3 b/api/nlopt_minimize.3
index 0e6631c89a7a9773f7d4e0b5795a8a02bfb95624..e8b5995aedbbf68ce996a514127f54814d87dba5 100644 (file)
--- a/api/nlopt_minimize.3
+++ b/api/nlopt_minimize.3
@@ -75,6 +75,7 @@ and others find only a local minimum.
 The
 .B nlopt_minimize
 function is a wrapper around several free/open-source minimization packages.
+as well as some new implementations of published optimization algorithms.
 You could, of course, compile and call these packages separately, and in
 some cases this will provide greater flexibility than is available via the
 .B nlopt_minimize
@@ -160,11 +161,18 @@ IEEE-754 floating-point infinity, which (in ANSI C99) is represented by
 the macro INFINITY in math.h.  Alternatively, for older C versions
 you may also use the macro HUGE_VAL (also in math.h).
 .sp
-With some of the algorithms, you may also employ arbitrary nonlinear
-constraints on the input variables.  This is indicated by returning NaN
-(not a number) or Inf (infinity) from your objective function whenever
-a forbidden point is requested.  See above for how to specify infinity;
-NaN is specified by the macro NAN in math.h (for ANSI C99).
+With some of the algorithms, especially those that do not require
+derivative information, a simple (but not especially efficient) way
+to implement arbitrary nonlinear constraints is to return Inf (see
+above) whenever the constraints are violated by a given input
+.IR x .
+More generally, there are various ways to implement constraints
+by adding "penalty terms" to your objective function, which are
+described in the optimization literature.
+A much more efficient way to specify nonlinear constraints is 
+provided by the
+.BR nlopt_minimize_constrained ()
+function (described in its own manual page).
 .SH ALGORITHMS
 The 
 .I algorithm
@@ -228,7 +236,10 @@ objective) for its local searches, and performs the global search by a
 branch-and-bound technique.  Only bound-constrained optimization
 is supported.  There is also another variant of this algorithm,
 .BR NLOPT_GD_STOGO_RAND ,
-which is a randomized version of the StoGO search scheme.
+which is a randomized version of the StoGO search scheme.  The StoGO
+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
 Perform a local (L) derivative-free (N) optimization, starting at
@@ -299,6 +310,18 @@ and
 for derivative/nonderivative searches, respectively).  There are also
 two other variants, \fBNLOPT_GD_MLSL\fR and \fBNLOPT_GN_MLSL\fR, which use
 pseudo-random numbers (instead of an LDS) as in the original MLSL algorithm.
+.TP
+.B NLOPT_LD_MMA
+Local (L) gradient-based (D) optimization using the method of moving
+asymptotes (MMA), or rather a refined version of the algorithm as
+published by Svanberg (2002).  (NLopt uses an independent free
+implementation of Svanberg's algorithm.)  The
+.B NLOPT_LD_MMA
+algorithm supports both bound-constrained and unconstrained optimization,
+and also supports an arbitrary number (\fIm\fR) of nonlinear constraints
+via the
+.BR nlopt_minimize_constrained ()
+function.
 .SH STOPPING CRITERIA
 Multiple stopping criteria for the optimization are supported, as
 specified by the following arguments to
@@ -439,3 +462,5 @@ termination condition that is consistently supported right now is
 Written by Steven G. Johnson.
 .PP
 Copyright (c) 2007 Massachusetts Institute of Technology.
+.SH "SEE ALSO"
+nlopt_minimize_constrained(3)