From: stevenj Date: Mon, 12 Jul 2010 23:01:44 +0000 (-0400) Subject: document SLSQP in man page X-Git-Url: http://www.chiark.greenend.org.uk/ucgi/~ianmdlvl/git?a=commitdiff_plain;h=c811a5c7592f80b7bcb9e7356e4a5840fdd22f36;p=nlopt.git document SLSQP in man page darcs-hash:20100712230144-c8de0-c5710fc6420742cf7ab493e51a7975064b3195de.gz --- diff --git a/api/nlopt.3 b/api/nlopt.3 index 8e13e69..c48b260 100644 --- a/api/nlopt.3 +++ b/api/nlopt.3 @@ -202,9 +202,9 @@ constant (so that you don't have to fill an array with a constant value): .SH NONLINEAR CONSTRAINTS Several of the algorithms in NLopt (MMA and ORIG_DIRECT) also support arbitrary nonlinear inequality constraints, and some also allow -nonlinear equality constraints (COBYLA, ISRES, and AUGLAG). For these -algorithms, you can specify as many nonlinear constraints as you wish -by calling the following functions multiple times. +nonlinear equality constraints (COBYLA, SLSQP, ISRES, and AUGLAG). +For these algorithms, you can specify as many nonlinear constraints as +you wish by calling the following functions multiple times. .sp In particular, a nonlinear inequality constraint of the form \fIfc\fR(\fIx\fR) <= 0, where the function @@ -441,9 +441,18 @@ published by Svanberg (2002). (NLopt uses an independent free-software/open-source 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 -as described above. +algorithm supports both bound-constrained and unconstrained +optimization, and also supports an arbitrary number (\fIm\fR) of +nonlinear inequality (not equality) constraints as described above. +.TP +.B NLOPT_LD_SLSQP +Local (L) gradient-based (D) optimization using sequential quadratic +programming and BFGS updates, supporting arbitrary nonlinear +inequality and equality constraints, based on the code by Dieter Kraft +(1988) adapted for use by the SciPy project. Note that this algorithm +uses dense-matrix methods requiring O(\fIn\fR^2) storage and +O(\fIn\fR^3) time, making it less practical for problems involving +more than a few thousand parameters. .TP .B NLOPT_LN_COBYLA Local (L) derivative-free (N) optimization using the COBYLA algorithm