On 01/07/2012 11:54 PM, Aaron Meurer wrote:
While we do have basic non-commutative differentiation implemented, we
don't have the rules involving things like transposes and norms
implemented yet. See
http://code.google.com/p/sympy/issues/detail?id=2759.
Aaron Meurer
2012/1/7 Ondřej Čertík<[email protected]>:
Hi John,
On Sat, Jan 7, 2012 at 1:52 PM, John B<[email protected]> wrote:
Hello. I am new to sympy and have been searching the docs for an hour
or so looking for a way to symbolically differentiate matrices or
vectors. Am I correct that there is currently no way to do this? As
an example, I would like to symbolically differentiate as follows:
n, i, j = symbols('n m i j k dim', integer=True)
i = Idx('i', n)
x = IndexedBase('x', shape=(n,1))
s = Sum(1/x[j,1]**2, (j, 0, n))
pprint(s)
ds = s.diff(x)
pprint(ds)
I think it's not implemented with IndexedBase. What should the result
be in this particular case?
You can differentiate each element of a Matrix class, but I guess
that's not what you want.
Ondrej
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Please define what you mean by derivative in terms of a limit. For
example if you have a scalar valued function of a vector do you mean the
gradient. With regard to a tensor (indexed array in general) what do
you mean? If you have a tensor function of multiple vectors or another
tensor what do you mean?
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