You could do the above by explicitly making a scalar symbol for each
of x[0], x[1], ...
>>> x,y,z = symbols('x y z')
>>> X = Matrix([[x], [y], [z]])
>>> f = 5*X[0]**2 + 5*X[1]**2 + 5*X[2]**2
>>> Matrix([f]).jacobian(X)
[10⋅x 10⋅y 10⋅z]
>>> Matrix([f]).jacobian(X) * ones(3,1) # inner product with ones vector to sum
[10⋅x + 10⋅y + 10⋅z]
I believe there is a clean way to make a bunch of symbols x0, x1, x2,
x3, ... easily using the symbols function. I just don't know it.
On Jan 8, 6:41 pm, Alan Bromborsky <[email protected]> wrote:
> On 01/08/2012 07:06 PM, John B wrote:
>
>
>
>
>
>
>
> >> You might find section 11.1 in "Geometric Algebra for Physicists" by
> >> Doran and Lasenby relevant. Also go
> >> tohttp://www.montgomerycollege.edu/Departments/planet/planet/Numerical_...
> >> and download bookGA.pdf. and look at chapter 6.
> > Hello Alan:
> > Thanks, but perhaps my example did not help to explain my real
> > question. Here is a more simple statement of my question. Is there a
> > way to use sympy to differentiate, for example, the function:
>
> > f = 5*x[0]**2 + 5*x[1]**2 + ...5*x[n]**2, where x is in vector or
> > matrix form to obtain the symbolic answer df/dx =
>
> > Sum(10*x[i], (i,0,n))
>
> > That is, is there some way to write in sympy (Sum(5*x[i]**2, (i,
> > 0,n))).diff()?
> > John
>
> Is x[j] a vector or matrix or is x = (x[0],...,x[n]) the vector for the
> vector case, then how would you write the matrix case?
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