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Jacobian of the linear transform Y = AX

  1. May 6, 2010 #1
    1. The problem statement, all variables and given/known data

    Y = AX = g(X)

    Where X,Y are elements of R^n and A is a nxn matrix.

    What is the Jacobian of this transformation, Jg(x)?

    2. Relevant equations


    3. The attempt at a solution

    Well, I know what to do in the non-matrix case. For example...

    U = g(x,y)
    V = h(x,y)

    The transformation can be seen as a vector valued function f(x,y) = (g(x,y),h(x,y)). So the jacobian of this transform, Jf(x,y) = the determinant of a matrix with row 1 = [dg/dx , dh/dx] and row 2 = [dg/dy, dh/dy].

    So Jf(x,y) = (dg/dx)(dh/dy) - (dg/dy)(dh/dx).

    But what do I do in the matrix case? I know g(X) can be seen as a vector with functions as entries but does this help?

    Y = AX = g(X) = (g1(X),g2(X),...,gn(X))

  2. jcsd
  3. May 6, 2010 #2


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    If you mean that A is a constant matrix, then the Jacobian is just the determinant 0f A.
  4. May 6, 2010 #3
    I agree with HallsofIvy:

    It don't think it matters to have a matrix, I do consider it as a vector of R^n as you do Legendre. Then I write the matrix of Jacobi of this function and find A; so it's jacobian is the determinant of A.
  5. May 6, 2010 #4
    Thanks guys. I wrote Ax, for a constant martix A, as a linear combination of its columns, then deduce that each of the gi(X) is a linear combination of the entries in the ith row of A. Then the jacobian is the determinant of A transposed, which is equal to the determinant of A!
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