def self_adjoint_eigvals(matrix, name=None):
"""Computes the eigenvalues a self-adjoint matrix.
Args:
matrix: `Tensor` of shape `[N, N]`.
name: string, optional name of the operation.
Returns:
e: Eigenvalues of `matrix`. Shape is `[N]`.
"""
e, _ = gen_linalg_ops.self_adjoint_eig_v2(matrix, compute_v=False, name=name)
return e
def self_adjoint_eigvals(matrix, name=None):
"""Computes the eigenvalues a self-adjoint matrix.
Args:
matrix: `Tensor` of shape `[N, N]`.
name: string, optional name of the operation.
Returns:
e: Eigenvalues of `matrix`. Shape is `[N]`.
"""
e, _ = gen_linalg_ops.self_adjoint_eig_v2(matrix, compute_v=False, name=name)
return e
def self_adjoint_eig(matrix, name=None):
"""Computes the eigen decomposition of a self-adjoint matrix.
Computes the eigenvalues and eigenvectors of an N-by-N matrix `matrix` such
that `matrix * v[:,i] = e(i) * v[:,i]`, for i=0...N-1.
Args:
matrix: `Tensor` of shape `[N, N]`.
name: string, optional name of the operation.
Returns:
e: Eigenvalues. Shape is `[N]`.
v: Eigenvectors. Shape is `[N, N]`. The columns contain the eigenvectors of
`matrix`.
"""
e, v = gen_linalg_ops.self_adjoint_eig_v2(matrix, compute_v=True, name=name)
return e, v
def self_adjoint_eig(matrix, name=None):
"""Computes the eigen decomposition of a self-adjoint matrix.
Computes the eigenvalues and eigenvectors of an N-by-N matrix `matrix` such
that `matrix * v[:,i] = e(i) * v[:,i]`, for i=0...N-1.
Args:
matrix: `Tensor` of shape `[N, N]`.
name: string, optional name of the operation.
Returns:
e: Eigenvalues. Shape is `[N]`.
v: Eigenvectors. Shape is `[N, N]`. The columns contain the eigenvectors of
`matrix`.
"""
e, v = gen_linalg_ops.self_adjoint_eig_v2(matrix, compute_v=True, name=name)
return e, v
def self_adjoint_eigvals(tensor, name=None):
"""Computes the eigenvalues of one or more self-adjoint matrices.
Note: If your program backpropagates through this function, you should replace
it with a call to tf.linalg.eigvalsh (possibly ignoring the second output) to
avoid computing the eigen decomposition twice. This is because the
eigenvectors are used to compute the gradient w.r.t. the eigenvalues. See
_SelfAdjointEigV2Grad in linalg_grad.py.
Args:
tensor: `Tensor` of shape `[..., N, N]`.
name: string, optional name of the operation.
Returns:
e: Eigenvalues. Shape is `[..., N]`. The vector `e[..., :]` contains the `N`
eigenvalues of `tensor[..., :, :]`.
"""
e, _ = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=False, name=name)
return e
def self_adjoint_eig(tensor, name=None):
"""Computes the eigen decomposition of a batch of self-adjoint matrices.
Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices
in `tensor` such that
`tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i]`, for i=0...N-1.
Args:
tensor: `Tensor` of shape `[..., N, N]`. Only the lower triangular part of
each inner inner matrix is referenced.
name: string, optional name of the operation.
Returns:
e: Eigenvalues. Shape is `[..., N]`. Sorted in non-decreasing order.
v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most
matrices contain eigenvectors of the corresponding matrices in `tensor`
"""
e, v = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=True, name=name)
return e, v
def self_adjoint_eigvals(tensor, name=None):
"""Computes the eigenvalues of one or more self-adjoint matrices.
Note: If your program backpropagates through this function, you should replace
it with a call to tf.self_adjoint_eig (possibly ignoring the second output) to
avoid computing the eigen decomposition twice. This is because the
eigenvectors are used to compute the gradient w.r.t. the eigenvalues. See
_SelfAdjointEigV2Grad in linalg_grad.py.
Args:
tensor: `Tensor` of shape `[..., N, N]`.
name: string, optional name of the operation.
Returns:
e: Eigenvalues. Shape is `[..., N]`. The vector `e[..., :]` contains the `N`
eigenvalues of `tensor[..., :, :]`.
"""
e, _ = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=False, name=name)
return e
def self_adjoint_eig(tensor, name=None):
"""Computes the eigen decomposition of a batch of self-adjoint matrices.
Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices
in `tensor` such that
`tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i]`, for i=0...N-1.
Args:
tensor: `Tensor` of shape `[..., N, N]`. Only the lower triangular part of
each inner inner matrix is referenced.
name: string, optional name of the operation.
Returns:
e: Eigenvalues. Shape is `[..., N]`. Sorted in non-decreasing order.
v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most
matrices contain eigenvectors of the corresponding matrices in `tensor`
"""
e, v = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=True, name=name)
return e, v
