Actually I was talking about this thing explained on page number 8 of
this document.

http://see.stanford.edu/materials/aimlcs229/cs229-notes1.pdf

I think implementation of this thing maybe a good start for fixing
issue 2759.

-Saurabh

On Apr 23, 4:21 pm, Alan Bromborsky <abro...@verizon.net> wrote:
> On 04/23/2013 12:32 AM, Aaron Meurer wrote:
>
>
>
>
>
>
>
> > Is this the same thing as the matrix derivative described in the
> > matrix cookbook (see
> >https://code.google.com/p/sympy/issues/detail?id=2759)? If so, then
> > the answer is no.
>
> > Aaron Meurer
>
> > On Sun, Apr 21, 2013 at 7:28 AM, Saurabh Jha <saurabh.j...@gmail.com> wrote:
> >> Hi,
>
> >> I wanted to know if something like this is already implemented in
> >> sympy?
>
> >> for a function f: R(m × n)--> R mapping from mapping from m × n
> >> matrices(let's say A) to real numbers than,
>
> >> ∇Af(A) =[∂f/∂A(11).............∂f/∂A(1n) ]
> >>              [                                        ]
> >>              [                                        ]
> >>              [∂f/∂A(n1)..............∂f/∂A(nn)]
>
> >> Note: with ∇Af(A), I meant taking gradient of   f(A) with respect to
> >> matrix A and with ∂f/∂A(11), I meant taking derivative of function f
> >> with respect to element A(11)  of matrix A.
>
> >> -Saurabh Jha
>
> >> --
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>
> Are you talking about the directional derivative of a matrix where if A
> is a matrix and f(A) is a function of the matrix then
>
> B\cdot\partial_{A}f = \lim_{h \rightarrow 0} f(A+hB)
>
> so if A is a m x n matrix you simply regard the matrix as a vector of
> length m x n.  The dot product is defined for
> m x n vectors.

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