Compute the eigenvalues and eigenvectors of a square array.
JAX implementation of numpy.linalg.eig().
a (ArrayLike) – array of shape (..., M, M) for which to compute the eigenvalues and vectors.
A tuple (eigenvalues, eigenvectors) with
eigenvalues: an array of shape (..., M) containing the eigenvalues.
eigenvectors: an array of shape (..., M, M), where column v[:, i] is the eigenvector corresponding to the eigenvalue w[i].
Notes
This differs from numpy.linalg.eig() in that the return type of jax.numpy.linalg.eig() is always complex64 for 32-bit input, and complex128 for 64-bit input.
At present, non-symmetric eigendecomposition is only implemented on the CPU and GPU backends. For more details about the GPU implementation, see the documentation for jax.lax.linalg.eig().
Examples
>>> a = jnp.array([[1., 2.],
... [2., 1.]])
>>> w, v = jnp.linalg.eig(a)
>>> with jax.numpy.printoptions(precision=4):
... w
Array([ 3.+0.j, -1.+0.j], dtype=complex64)
>>> v
Array([[ 0.70710677+0.j, -0.70710677+0.j],
[ 0.70710677+0.j, 0.70710677+0.j]], dtype=complex64)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4