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From: Chyh-chong Chuang <ccchuang@gate.sinica.edu.tw>
To: chemistry@ccl.net
Cc: str-nmr@net.bio.net, molmodel@net.bio.net, bio-soft@net.bio.net
Subject: FFTRC sub-rountine
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Dear netters:

	When I compiled a fortran code, I met a message 
'Unresolved:fftrc_'. After checking the source code, I found there is a 
funtion call exist: 'call FFTRC(tmfct,nptm,fqfct1,iwk,xxx)'.
	Does any one know where I can get this subroutine, it is sure 
that our mechine does not have this routine.

Thanks all

ccchuang 

  ============================================
    Chyh-Chong Chuang
    Institude of Biological Chemistry, 
    Academia Sinica,Taipei, Taiwan
  --------------------------------------------
    Phone: 886-2-7858981 ext 7091 
    Fax: 886-2-7883473
    E-Mail: ccchuang@gate.sinica.edu.tw
  ============================================


From states@ibc.WUStL.EDU  Wed Mar  6 09:32:41 1996
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Date: Wed, 6 Mar 1996 07:50:41 -0600
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Subject: Re: CCL:Conservation of Difficulty
To: <young@argus.cem.msu.edu>, chemistry@www.ccl.net
X-Mailer: AIR Mail 3.X (SPRY, Inc.)


Equivalence of computational complexity is well known.  If you could solve
the traveling salesman problem, you could solve many other difficult computing
problems.  By gross extrapolation, not only have alot of smart people been
trying to find simple analytical solutions to the solutions to the many body 
Schroedinger equation without success, many more people in other fields have 
been working on problems that are computationally equivalent, also without 
success.  If there is a simple solution, finding it certainly is not easy!

That most of the problems we have solved tend to have simple solutions 
(Occam's
razor), does not imply that all problems have simple solutions.  Maybe we just
are not smart enough to solve the problems with really complicated solutions. 
 The conclusion from computer science is that there seem to be whole classes 
of problems that are just plain hard.

David

From hebant@ext.jussieu.fr  Wed Mar  6 11:32:51 1996
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To: CHEMISTRY@www.ccl.net
From: hebant@ext.jussieu.fr (Pascal HEBANT)
Subject: Compiling DeMon 


Hi all,

Is it possible to compile DeMon with a maximum number of orbitals higher
than 600? And if it is, where do I have to make the changes in the source?
Regards

Pascal





*****************************************************************************

Pascal HEBANT

Laboratoire d'Electrochimie et de Chimie Analytique
Ecole Nationale Superieure de Chimie de Paris
11 rue Pierre et Marie Curie
75005 Paris FRANCE

tel: 33 (1) 44 27 66 94                             fax: 33 (1) 44 27 67 50

http://alcyone.enscp.jussieu.fr/Pages/LECA/Electrochimie.html
*****************************************************************************





From hinsenk@ERE.UMontreal.CA  Wed Mar  6 11:44:52 1996
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CC: chemistry@www.ccl.net
In-reply-to: <199603060204.VAA29033@slater.cem.msu.edu> (young@argus.cem.msu.edu)
Subject: Re: CCL:Conservation of Difficulty
From: hinsenk@ERE.UMontreal.CA (Konrad HINSEN)


> right.  I firmly believe that the Schrodinger equation will one day
> be as easily dealt with as trignometric functions on a calculator.

Which means by numerical approximation ;-)

Seriously, I wonder what your confidence is based on. Of course it may
be possible to find further analytic solutions, but I don't see why
this should necessarily be true. Much less do I see how you can make
such a claim for *all* applications of the Schroedinger equation.

Of course all this depends on what you call a "solution". In the case
of trigonometry, all we have is a set of analytic relations between
various functions that often occur together. With the exception of a
few special cases, there is no "analytic" answer to a trigonometric
problem, in the sense that the result cannot be expressed in simpler
functions (i.e. sums, products, and powers). A comparative level of
"solution" of the Schroedinger equation would be a safe numerical
procedure that can find the solution to any problem to a specified
accuracy. This procedure could then be put into the theoretical
chemist's equivalent of a pocket calculator. If that's what you mean
by solution, I agree that it will probably one day be available.

-------------------------------------------------------------------------------
Konrad Hinsen                     | E-Mail: hinsenk@ere.umontreal.ca
Departement de chimie             | Tel.: +1-514-343-6111 ext. 3953
Universite de Montreal            | Fax:  +1-514-343-7586
C.P. 6128, succ. Centre-Ville     | Deutsch/Esperanto/English/Nederlands/
Montreal (QC) H3C 3J7             | Francais (phase experimentale)
-------------------------------------------------------------------------------

From owner-chemistry@ccl.net  Wed Mar  6 13:32:49 1996
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From: gilson@indigo14.carb.nist.gov (Michael K. Gilson)
Subject: Conservation of Difficulty
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The discussion raises the following possibly naive question:

Are there mathematical problems that can be solved analytically,
whose numerical solutions look like hard, NP-complete problems?

Mike


From owner-chemistry@ccl.net  Wed Mar  6 13:41:42 1996
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From: "Gregor Fels" <GF@chemie.uni-paderborn.de>
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Date:          Wed, 6 Mar 1996 18:57:27 GST
Subject:       parallel computing
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Could somebody please point me to literature on using parallel 
computing for semiempirical and/or ab initio calculation.

Thanks in advance,
Gregor


Dr. Gregor Fels
Universitaet-GH-Paderborn
FB 13-Org. Chemie
Warburgerstr. 100
D-33098 Paderborn, Germany

Tel. 0049-5251-602181/Fax -603245
EMail GF@chemie.uni-paderborn.de

From hinsenk@ERE.UMontreal.CA  Wed Mar  6 14:32:44 1996
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To: chemistry@www.ccl.net, python-list@cwi.nl
Subject: More Python code for computational chemistry
From: hinsenk@ERE.UMontreal.CA (Konrad HINSEN)


I have made some major additions to the Python code for computational
chemistry that I published earlier on the CCL archive. The most
interesting addition is probably the code that automatically
calculates gradients and force constants for an arbitrary potential
energy function (these are analytic derivatives, not finite-difference
calculations). There are also some minor improvements in the PDB
code; it can now handle compressed files and understands filenames
containing "~" to refer to a home directory (the last two features
are for Unix only).

The code can be found via the URLs

  ftp://www.ccl.net:/pub/chemistry/software/SOURCES/PYTHON/HINSEN
  gopher://www.ccl.net:73/11/software/SOURCES/PYTHON/HINSEN

Here is a short description of the modules:


PDB.py  (requires TextFile.py, Vector.py and umath.py)

  High-level I/O with PDB files. Allows you to deal with PDB files as if they
  were lists of residues and atoms. Example:
     for residue in PDBConfiguration('protein.pdb'):
         for atom in residue:
             print atom.name, atom.position


TextFile.py

  Unified access to compressed and uncompressed files, plus access
  to text files as a sequence of lines. ONLY FOR UNIX SYSTEMS!


Derivatives.py (requires Vector.py and umath.py)

  Automatic derivatives. Calculates analytical derivatives numerically, given
  only the original function, to any order and for any number of
  variables. Example:
     x = DerivVar(1.1,0)
     y = DerivVar(0.44, 1)
     f = exp(x**2+y**2)
     print f
  prints:
     (4.06982499598, [8.95361499116, 3.58144599646])


FirstDerivatives.py (requires Vector.py and umath.py)

  Automatic first-order derivatives. This module is a subset of
  Derivatives.py and compatible as far as possible. The restriction
  to first-order derivatives makes it faster.


Potential.py (requires Derivatives.py, FirstDerivatives.py, Vector.py
              and umath.py)

  Automatic gradient and force constant calculation for potential
  functions (or any function).
  Example:
     def harmonic(k, r1, r2):
          dr = r1-r2
	 return k*dr*dr
     harmonic_g = PotentialWithGradientsAndForceConstants(harmonic)
     energy, gradients, force_constants = \
            harmonic_g(100., Vector(0,3,1), Vector(1,2,0))
     print energy
     print gradients
     print force_constants
  prints:
     300.0
     [Vector(-200.0,200.0,200.0), Vector(200.0,-200.0,-200.0)]
     [[200.0, 0.0, 0.0, -200.0, 0.0, 0.0],
      [0.0, 200.0, 0.0, 0.0, -200.0, 0.0],
      [0.0, 0.0, 200.0, 0.0, 0.0, -200.0],
      [-200.0, 0.0, 0.0, 200.0, 0.0, 0.0],
      [0.0, -200.0, 0.0, 0.0, 200.0, 0.0],
      [0.0, 0.0, -200.0, 0.0, 0.0, 200.0]]


Vector.py (requires umath.py)

  3d-vectors, used by the three other modules.


umath.py

  Simplified substitute for the module "umath" from the
  numerics package. Use only if you don't have the
  numerics package. This module is used by all the other
  modules in this directory.

-------------------------------------------------------------------------------
Konrad Hinsen                     | E-Mail: hinsenk@ere.umontreal.ca
Departement de chimie             | Tel.: +1-514-343-6111 ext. 3953
Universite de Montreal            | Fax:  +1-514-343-7586
C.P. 6128, succ. Centre-Ville     | Deutsch/Esperanto/English/Nederlands/
Montreal (QC) H3C 3J7             | Francais (phase experimentale)
-------------------------------------------------------------------------------

