From: stevenj Date: Fri, 20 Jul 2012 20:20:35 +0000 (-0400) Subject: document CCSA X-Git-Url: http://www.chiark.greenend.org.uk/ucgi/~ianmdlvl/git?a=commitdiff_plain;h=d50c59a3dcad8fabbe3014bd1aac17978842a806;p=nlopt.git document CCSA Ignore-this: 81c81ee37e294f64a00358c52c3cf883 darcs-hash:20120720202035-c8de0-349519e0f396880f305dee17f6490d609bffbd3c.gz --- diff --git a/mma/README b/mma/README index 01c57c3..cb87086 100644 --- a/mma/README +++ b/mma/README @@ -11,8 +11,22 @@ algorithms in NLopt, in that it handles an arbitrary set of nonlinear I've implemented the full nonlinear-constrained MMA algorithm, and it is exported under the nlopt_minimize_constrained API. +I also implemented another CCSA algorithm from the same paper: instead of +constructing local MMA approximations, it constructs simple quadratic +approximations (or rather, affine approximations plus a quadratic penalty +term to stay conservative). This is the ccsa_quadratic code. It seems +to have similar convergence rates to MMA for most problems, which is not +surprising as they are both essentially similar. However, for the quadratic +variant I implemented the possibility of preconditioning: including a +user-supplied Hessian approximation in the local model. It is easy to +incorporate this into the proof in Svanberg's paper, and to show that +global convergence is still guaranteed as long as the user's "Hessian" +is positive semidefinite, and it practice it can greatly improve convergence +if the preconditioner is a good approximation for (at least for the +largest eigenvectors) the real Hessian. + It is under the same MIT license as the rest of my code in NLopt (see ../COPYRIGHT). Steven G. Johnson -July 2008 +July 2008 - July 2012