🚀 go-pugleaf

In article <1993Jan22.081540.1700@atri.curtin.edu.au>,
thng@atri.curtin.edu.au (Ian Li-Jin Thng) writes:

>G'day to all symbolic netters,
>
>I'm seeking assistance on anyone who has experience with or have
>come across references regarding symbolic programming using Fortran 
>77 . 
>
>I know that a common method of symbolic programming is to use 
>pointer structures which Fortran 77 do not support. 
>
>I have at present a Fortran program which is numerically intensive 
>at the moment. I need to add a section to that program which is 
>unfortunately symbolically intensive. The requirements of the 
>symbolically intensive portion are as follows :
>
>Essentially to find out the symbolic expressions for the partial 
>derivatives [...]

There is a package called JAKEF in the NETLIB collection which will
perform "automatic differentiation" on scalar- and vector-valued
functions of several variables, thereby yielding the gradient and
Jacobian, respectively.  The results are exact up to round-off error.
(I will post an "Introduction to NETLIB" document separately to this
news group.)

For symbolic differentiation in FORTRAN (often desirable for
optimization routines which need Jacobians and sometimes Hessians),
you might want to search the literature on the topic "automatic
differentiation", which refers to the process of taking a subroutine
that computes a given differentiable function and automatically
producing a second subroutine that computes the derivative of that
function by an exact rather than approximate method.  (There is a
description of this in the Spring '92 issue of the Maple Technical
Newsletter.)  You might also want to query sci.math.num-analysis on
the topic of "automatic differentiation".

Good luck,

Frederick W. Chapman
Senior User Consultant
Lehigh University Computing Center

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