Ryan D. Alexander wrote:
> Monte-Carlo methods and simulated annealing are good for monomodal
mathematical functions. But is it really the case in MM ? Two ways :
One random based "the genetic algoritms", David E; Goldberg, Addison
Wesley can be benefit because you have not only the "minimum minimorum"
but also the "local minima" of a multimodal function. The most robust
way, a little less expensive, can be the flexible simplex methods
(from G.E.P. Box ? if my memory is right).
Friendly. Yves.
> In attempting to model chromosomes packed within a nucleus, I've decided that I
> need a type of Monte Carlo Minimization routine. Scouring the Web has turned up
> little in the way progress, so I've turned to you all in NG-Land...
>> Essentially I'm looking for an algorithm (in Fortran 77/90, preferably) where I
> may enter a set of initial cartesian coordinates, the associated radii for each
> "chromosome," and an objective function (i.e. Lennard-Jones potential
> function). Let the program calculate the value of my objective function, Monte
> Carlo (smartly) to re-position the chromosomes, recalculate the potential, etc.,
> etc. until the function value is minimized.
>> Anyone's help (books, articles, URL's) would be greatly appreciated!! If anyone
> in the realm of molecular dynamics, or packing theory (of non-uniform shapes)
> have any ideas, I'd obviously welcome them as well.
>> Thanks in advance... (oh & as this is probably not of general interest to these
> ng's please just e-mail me directly if you'd like)
> -- Ryan D. Alexander
> =========================================================
> Ryan D. Alexander, CSU-LBNL NSCORT Ph.D. Trainee
> Colorado State Univ., Dept. of Radiological Health Sciences
> E-mail: ryanalex at lamar.colostate.edu> Office Ph: 970-491-0520, Dept. Fax: 970-491-0623
> =========================================================
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