May I ask in what context you require the matrix derivatives.
Depending on what you are after an Algorithmic Differentiation (AD)
tool could also be a viable alternative.
With an AD tool you could differentiate functions (i.e. compute
gradient, Jacobian, Hessian, higher order tensors) very easily and
efficiently.
An example of what functions are possible:
def f(A,x):
for n in range(30):
y = dot(x.T,dot(A,x))
A = inv(A) - dot(x,x.T) * y
return trace(A)
best regards,
Sebastian
On May 4, 10:32 am, janwillem <jwevand...@xs4all.nl> wrote:
> I need some explanation on the workings of SymPy. As an example the
> following script:
> import sympy
> X, F, B = sympy.symbols('XFB')
> Y = X / F - B #eqn 1
> DY = sympy.Matrix(sympy.diff(Y, (X, F, B))).T
> print DY
>
> I had expected (eqn 2): [1/F, -X/F**2, -1]
> But get: [D(-B + X/F, X), D(-B + X/F, F), D(-B + X/F, B)]
>
> Not after doing a trick of which I cannot remember why I tried it, I
> get the desired result
> DY = DY.subs({X:X, F:F, B:B})
> From the doc I had thought that DY.doin() would work but that gives
> "raise AttributeError()".
> So there is obviously something I do not understand, please some help
> Janwillem
>
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