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scipy.linalg.

# expm

scipy.linalg.expm(A)

  Compute the matrix exponential of an array.

  Parameters:
    A ndarray

      Input with last two dimensions are square `(..., n, n)`.

  Returns:
    eA ndarray

      The resulting matrix exponential with the same shape of `A`

  Notes

  Implements the algorithm given in [1], which is essentially a Pade approximation with a variable order that is decided based on the array data.

  For input with size `n`, the memory usage is in the worst case in the order of `8*(n**2)`. If the input data is not of single and double precision of real and complex dtypes, it is copied to a new array.

  For cases `n >= 400`, the exact 1-norm computation cost, breaks even with 1-norm estimation and from that point on the estimation scheme given in [2] is used to decide on the approximation order.

  References

  [1]

  Awad H. Al-Mohy and Nicholas J. Higham, (2009), “A New Scaling and Squaring Algorithm for the Matrix Exponential”, SIAM J. Matrix Anal. Appl. 31(3):970-989, DOI:10.1137/09074721X

  [2]

  Nicholas J. Higham and Francoise Tisseur (2000), “A Block Algorithm for Matrix 1-Norm Estimation, with an Application to 1-Norm Pseudospectra.” SIAM J. Matrix Anal. Appl. 21(4):1185-1201, DOI:10.1137/S0895479899356080

  Examples

      >>> import numpy as np
      >>> from scipy.linalg import expm, sinm, cosm

  Matrix version of the formula exp(0) = 1:

      >>> expm(np.zeros((3, 2, 2)))
      array([[[1., 0.],
              [0., 1.]],

             [[1., 0.],
              [0., 1.]],

             [[1., 0.],
              [0., 1.]]])

  Euler’s identity (exp(i*theta) = cos(theta) + i*sin(theta)) applied to a matrix:

      >>> a = np.array([[1.0, 2.0], [-1.0, 3.0]])
      >>> expm(1j*a)
      array([[ 0.42645930+1.89217551j, -2.13721484-0.97811252j],
             [ 1.06860742+0.48905626j, -1.71075555+0.91406299j]])
      >>> cosm(a) + 1j*sinm(a)
      array([[ 0.42645930+1.89217551j, -2.13721484-0.97811252j],
             [ 1.06860742+0.48905626j, -1.71075555+0.91406299j]])