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Re: NLSCON


From: Bill Denney
Subject: Re: NLSCON
Date: Thu, 18 Aug 2005 13:31:11 -0400 (EDT)

In addition to this, there are free solutions available from netlib like nnls that could probably be rather easily added (I know that we already have lsode).

Bill

On Thu, 18 Aug 2005, address@hidden wrote:

3) are there other plans to support constrained, non-linear
optimisation techniques ?

Yes, there are two I know about.

(1) SQP: John has a sequential quadratic programming solution already
   integrated into Octave (cf. optimization/sqp.m).  sqp doc states

   ## Solve the nonlinear program
   ##      min phi (x)
   ##       x
   ## subject to
   ##      g(x)  = 0
   ##      h(x) >= 0
   ## using a successive quadratic programming method.

(2) SOLNP: I have secured permission from Yinyu Ye to release his
   self-contained "SOLNP" dense constrained nonlinear solver, under
   GPL.  The problem formulation for it is as follows.

   ##         min f(x)
   ##          x
   ## subject to
   ##             g(x) = 0
   ##     lo_h <= h(x) <= hi_h
   ##     lo_x <=   x  <= hi_x
   ##
   ## In general, $f$, $g$ and $h$ are any nonlinear smooth functions
   ## that may be specified and computed in Octave.

   SOLNP is just two functions, written entirely in Octave.  It is
   simpler to use, works well, and ran twice as fast as a
   reimplementation of the method-of-multipliers I wrote.

   I've heavily commented and restructured the code, added diagnostics,
   added references, corrected confusing typo's, and made minor changes
   to avoid globals (but as far as I can tell, the code was bug free,
   so what's out there is safe to use), and created a
   developer-friently test suite for it.

   And, due largely to my daughter, the port of the (substantial) SOLNP
   documentation to txi is almost done (info done, going back and
   adding the TeX specific stuff); also still need to create some
   automatic validation to run as part of a build.

   I expect to release SOLNP into the Octave source stream by 30Sep05.

Hope this helps.

/Jskud


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"When it comes time for college, I promise that my daughter can go to the
finest school there is-- in South Carolina."
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