jax.scipy.linalg.expm#
- jax.scipy.linalg.expm(A, *, upper_triangular=False, max_squarings=16)[source]#
Compute the matrix exponential of an array.
LAX-backend implementation of
scipy.linalg._matfuncs.expm()
.In addition to the original NumPy argument(s) listed below, also supports the optional boolean argument
upper_triangular
to specify whether theA
matrix is upper triangular, and the optional argumentmax_squarings
to specify the max number of squarings allowed in the scaling-and-squaring approximation method. Return nan if the actual number of squarings required is more thanmax_squarings
.The number of required squarings = max(0, ceil(log2(norm(A)) - c) where norm() denotes the L1 norm, and
c=2.42 for float64 or complex128,
c=1.97 for float32 or complex64
Original docstring below.
- Parameters:
A (ndarray) – Input with last two dimensions are square
(..., n, n)
.- Returns:
eA – The resulting matrix exponential with the same shape of
A
- Return type:
ndarray
References