From: ingber@ALUMNI.CCO.CALTECH.EDU (Lester Ingber)
Newsgroups: misc.invest
Subject: 2nd Request for (p)Reprints on Simulated Annealing
Date: 12 Sep 92 19:15:05 GMT
Reply-To: ingber@alumni.cco.caltech.edu

2nd Request for (p)Reprints on Simulated Annealing

Since I've published some work using these methods in finance, and
since I have received papers and comments from people in
finance/trading/economics who also are using these methods, I am
posting this here.

I posted the text below in July, and have received many interesting
papers which I will at least mention in my review.  It is clear
that many researchers use something "like" simulated annealing (SA)
in their work to approach quite difficult computational problems.
They take advantage of the ease of including complex constraints and
nonlinearities into an SA approach that requires a quite simple and
small code, especially relative to many other search algorithms.

However, the bulk of the papers I have seen use the standard Boltzmann
annealing, for which it has been proven sufficient to only use a
log annealing schedule for the temperature parameter in order to
statistically achieve a global optimal solution.  This can require
a great deal of CPU time to implement, and so these papers actually
"quench" their searches by using much faster temperature schedules,
too fast to theoretically claim they are achieving the global optimum.
Instead they have defined their own method of simulated quenching (SQ).

In many of their problems this really is not much of an issue, as there
is enough additional information about their system to be able to claim
that their SQ is good enough, and the ease of implementation certainly
warrants its use.  I.e., anyone familiar with trying to use other
"standard" methods of nonlinear optimization on difficult problems
will appreciate this.  I also appreciate that faster SA methods,
such as I have published myself, are not as easily implemented.

I would like to have more examples of:
(1) papers that have really used SA instead of SQ in difficult
problems.
(2) proposed/tested improvements to SA which still have the important
feature of establishing at least a heuristic argument that a global
optimum can indeed be reached, e.g., some kind of ergodic argument.

The review is on SA, and I do not have the allotted space or intention
to compare SA to other important and interesting algorithms.

Thanks.

Lester

}I have accepted an invitation to prepare a review article on simulated
}annealing for Statistics and Computing.  The first draft is due 15
}Jan 93.
}
}If you or your colleagues have performed some unique work using
}this methodology that you think could be included in this review,
}please send me (p)reprints via regular mail.  As I will be
}making an effort to prepare a coherent article, not necessarily an
}all inclusive one, please do not be too annoyed if I must choose not
}to include/reference work you suggest.  Of course, I will formally
}reference or acknowledge any inclusion of your suggestions/material
}in this paper.  While there has been work done, and much more remains
}to be done, on rigorous proofs and pedagogical examples/comparisons,
}I plan on stressing the use of this approach on complex, nonlinear
}and even stochastic systems.
}
}I am a "proponent" of a statistical mechanical approach to selected
}problems in several fields; some recent reprints are available via
}anonymous ftp from ftp.umiacs.umd.edu [128.8.120.23] in the pub/ingber
}directory.  I am not a hardened "proponent" of simulated annealing;
}I welcome papers criticizing or comparing simulated annealing to
}other approaches.  I already plan on including some references that
}are openly quite hostile to this approach.

  #               Prof. Lester Ingber               #
  #            ingber@alumni.caltech.edu            #
  #  P.O. Box 857                                   #
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Newsgroups: misc.invest