Add eigvals to LinearOperator. This will return the eigenvalues of a self-adjoint operator.

PiperOrigin-RevId: 274689673

tensorflow/python/kernel_tests/linalg/linear_operator_circulant_test.py

@@ -257,7 +257,7 @@ class LinearOperatorCirculantTestNonHermitianSpectrum(
   # spectra.
   @staticmethod
   def skip_these_tests():
-    return ["cholesky"]
+    return ["cholesky", "eigvalsh"]
 
   def operator_and_matrix(
       self, shape_info, dtype, use_placeholder,
@@ -541,7 +541,7 @@ class LinearOperatorCirculant2DTestNonHermitianSpectrum(
 
   @staticmethod
   def skip_these_tests():
-    return ["cholesky"]
+    return ["cholesky", "eigvalsh"]
 
   def operator_and_matrix(
       self, shape_info, dtype, use_placeholder,

tensorflow/python/kernel_tests/linalg/linear_operator_composition_test.py

@@ -48,16 +48,24 @@ class SquareLinearOperatorCompositionTest(
     # Cholesky not implemented.
     return ["cholesky"]
 
-  def operator_and_matrix(self, build_info, dtype, use_placeholder):
+  def operator_and_matrix(self, build_info, dtype, use_placeholder,
+                          ensure_self_adjoint_and_pd=False):
     shape = list(build_info.shape)
 
     # Either 1 or 2 matrices, depending.
     num_operators = rng.randint(low=1, high=3)
-    matrices = [
-        linear_operator_test_util.random_positive_definite_matrix(
-            shape, dtype, force_well_conditioned=True)
-        for _ in range(num_operators)
-    ]
+    if ensure_self_adjoint_and_pd:
+      # The random PD matrices are also symmetric. Here we are computing
+      # A @ A ... @ A. Since A is symmetric and PD, so are any powers of it.
+      matrices = [
+          linear_operator_test_util.random_positive_definite_matrix(
+              shape, dtype, force_well_conditioned=True)] * num_operators
+    else:
+      matrices = [
+          linear_operator_test_util.random_positive_definite_matrix(
+              shape, dtype, force_well_conditioned=True)
+          for _ in range(num_operators)
+      ]
 
     lin_op_matrices = matrices
 
@@ -68,6 +76,8 @@ class SquareLinearOperatorCompositionTest(
 
     operator = linalg.LinearOperatorComposition(
         [linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices],
+        is_positive_definite=True if ensure_self_adjoint_and_pd else None,
+        is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
         is_square=True)
 
     matmul_order_list = list(reversed(matrices))

tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py

@@ -199,7 +199,7 @@ class LinearOperatorLowRankUpdatetestWithDiagCannotUseCholesky(
 
   @staticmethod
   def skip_these_tests():
-    return ["cholesky"]
+    return ["cholesky", "eigvalsh"]
 
   _use_diag_update = True
   _is_diag_update_positive = False
@@ -242,7 +242,7 @@ class LinearOperatorLowRankUpdatetestNoDiagCannotUseCholesky(
 
   @staticmethod
   def skip_these_tests():
-    return ["cholesky"]
+    return ["cholesky", "eigvalsh"]
 
   _use_diag_update = False
   _is_diag_update_positive = None

tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py

@@ -38,19 +38,28 @@ class LinearOperatorLowerTriangularTest(
     # Cholesky does not make sense for triangular matrices.
     return ["cholesky"]
 
-  def operator_and_matrix(self, build_info, dtype, use_placeholder):
+  def operator_and_matrix(self, build_info, dtype, use_placeholder,
+                          ensure_self_adjoint_and_pd=False):
     shape = list(build_info.shape)
     # Upper triangle will be nonzero, but ignored.
     # Use a diagonal that ensures this matrix is well conditioned.
     tril = linear_operator_test_util.random_tril_matrix(
         shape, dtype=dtype, force_well_conditioned=True, remove_upper=False)
+    if ensure_self_adjoint_and_pd:
+      # Get the diagonal and make the matrix out of it.
+      tril = array_ops.matrix_diag_part(tril)
+      tril = math_ops.abs(tril) + 1e-1
+      tril = array_ops.matrix_diag(tril)
 
     lin_op_tril = tril
 
     if use_placeholder:
       lin_op_tril = array_ops.placeholder_with_default(lin_op_tril, shape=None)
 
-    operator = linalg.LinearOperatorLowerTriangular(lin_op_tril)
+    operator = linalg.LinearOperatorLowerTriangular(
+        lin_op_tril,
+        is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
+        is_positive_definite=True if ensure_self_adjoint_and_pd else None)
 
     matrix = array_ops.matrix_band_part(tril, -1, 0)
 

tensorflow/python/kernel_tests/linalg/linear_operator_permutation_test.py

@@ -50,7 +50,7 @@ class LinearOperatorPermutationTest(
   @staticmethod
   def skip_these_tests():
     # This linear operator is almost never positive definite.
-    return ["cholesky"]
+    return ["cholesky", "eigvalsh"]
 
   def operator_and_matrix(
       self, build_info, dtype, use_placeholder,

tensorflow/python/ops/linalg/linear_operator.py

@@ -1052,6 +1052,28 @@ class LinearOperator(module.Module):
       self._check_input_dtype(x)
       return self._add_to_tensor(x)
 
+  def _eigvals(self):
+    return linalg_ops.self_adjoint_eigvals(self.to_dense())
+
+  def eigvals(self, name="eigvals"):
+    """Returns the eigenvalues of this linear operator.
+
+    If the operator is marked as self-adjoint (via `is_self_adjoint`)
+    this computation can be more efficient.
+
+    Note: This currently only supports self-adjoint operators.
+
+    Args:
+      name:  A name for this `Op`.
+
+    Returns:
+      Shape `[B1,...,Bb, N]` `Tensor` of same `dtype` as `self`.
+    """
+    if not self.is_self_adjoint:
+      raise NotImplementedError("Only self-adjoint matrices are supported.")
+    with self._name_scope(name):
+      return self._eigvals()
+
   def _can_use_cholesky(self):
     return self.is_self_adjoint and self.is_positive_definite
 

tensorflow/python/ops/linalg/linear_operator_adjoint.py

@@ -211,3 +211,9 @@ class LinearOperatorAdjoint(linear_operator.LinearOperator):
 
   def _add_to_tensor(self, x):
     return self.to_dense() + x
+
+  def _eigvals(self):
+    eigvals = self.operator.eigvals()
+    if not self.operator.is_self_adjoint:
+      eigvals = math_ops.conj(eigvals)
+    return eigvals

tensorflow/python/ops/linalg/linear_operator_block_diag.py

@@ -349,6 +349,15 @@ class LinearOperatorBlockDiag(linear_operator.LinearOperator):
     return control_flow_ops.group([
         operator.assert_positive_definite() for operator in self.operators])
 
+  def _eigvals(self):
+    eig_list = []
+    for operator in self.operators:
+      # Extend the axis for broadcasting.
+      eig_list += [operator.eigvals()[..., array_ops.newaxis]]
+    eig_list = linear_operator_util.broadcast_matrix_batch_dims(eig_list)
+    eigs = array_ops.concat(eig_list, axis=-2)
+    return array_ops.squeeze(eigs, axis=-1)
+
   def _split_input_into_blocks(self, x, axis=-1):
     """Split `x` into blocks matching `operators`'s `domain_dimension`.
 

tensorflow/python/ops/linalg/linear_operator_circulant.py

@@ -749,6 +749,9 @@ class LinearOperatorCirculant(_BaseLinearOperatorCirculant):
         is_square=is_square,
         name=name)
 
+  def _eigvals(self):
+    return ops.convert_to_tensor(self.spectrum)
+
 
 @tf_export("linalg.LinearOperatorCirculant2D")
 class LinearOperatorCirculant2D(_BaseLinearOperatorCirculant):

tensorflow/python/ops/linalg/linear_operator_diag.py

@@ -244,6 +244,9 @@ class LinearOperatorDiag(linear_operator.LinearOperator):
     new_diag = self._diag + x_diag
     return array_ops.matrix_set_diag(x, new_diag)
 
+  def _eigvals(self):
+    return ops.convert_to_tensor(self.diag)
+
   @property
   def diag(self):
     return self._diag

tensorflow/python/ops/linalg/linear_operator_householder.py

@@ -240,6 +240,17 @@ class LinearOperatorHouseholder(linear_operator.LinearOperator):
         reflection_axis, axis=-1, keepdims=True)
     return 1. - 2 * normalized_axis * math_ops.conj(normalized_axis)
 
+  def _eigvals(self):
+    # We have (n - 1) +1 eigenvalues and a single -1 eigenvalue.
+    result_shape = array_ops.shape(self.reflection_axis)
+    n = result_shape[-1]
+    ones_shape = array_ops.concat([result_shape[:-1], [n - 1]], axis=-1)
+    neg_shape = array_ops.concat([result_shape[:-1], [1]], axis=-1)
+    eigvals = array_ops.ones(shape=ones_shape, dtype=self.dtype)
+    eigvals = array_ops.concat(
+        [-array_ops.ones(shape=neg_shape, dtype=self.dtype), eigvals], axis=-1)
+    return eigvals
+
   @property
   def reflection_axis(self):
     return self._reflection_axis

tensorflow/python/ops/linalg/linear_operator_identity.py

@@ -399,6 +399,9 @@ class LinearOperatorIdentity(BaseLinearOperatorIdentity):
       new_diag = 1 + mat_diag
       return array_ops.matrix_set_diag(mat, new_diag)
 
+  def _eigvals(self):
+    return self._ones_diag()
+
   def _check_num_rows_possibly_add_asserts(self):
     """Static check of init arg `num_rows`, possibly add asserts."""
     # Possibly add asserts.
@@ -724,6 +727,9 @@ class LinearOperatorScaledIdentity(BaseLinearOperatorIdentity):
 
       return array_ops.matrix_set_diag(mat, new_diag)
 
+  def _eigvals(self):
+    return self._ones_diag() * self.multiplier[..., array_ops.newaxis]
+
   @property
   def multiplier(self):
     """The [batch] scalar `Tensor`, `c` in `cI`."""

tensorflow/python/ops/linalg/linear_operator_inversion.py

@@ -196,3 +196,6 @@ class LinearOperatorInversion(linear_operator.LinearOperator):
 
   def _solve(self, rhs, adjoint=False, adjoint_arg=False):
     return self.operator.matmul(rhs, adjoint=adjoint, adjoint_arg=adjoint_arg)
+
+  def _eigvals(self):
+    return 1. / self.operator.eigvals()

tensorflow/python/ops/linalg/linear_operator_kronecker.py

@@ -522,7 +522,7 @@ class LinearOperatorKronecker(linear_operator.LinearOperator):
   def _to_dense(self):
     product = self.operators[0].to_dense()
     for operator in self.operators[1:]:
-      # Product has shape [B, R1, 1, C1].
+      # Product has shape [B, R1, 1, C1, 1].
       product = product[
           ..., :, array_ops.newaxis, :, array_ops.newaxis]
       # Operator has shape [B, 1, R2, 1, C2].
@@ -541,6 +541,25 @@ class LinearOperatorKronecker(linear_operator.LinearOperator):
     product.set_shape(self.shape)
     return product
 
+  def _eigvals(self):
+    # This will be the kronecker product of all the eigenvalues.
+    # Note: It doesn't matter which kronecker product it is, since every
+    # kronecker product of the same matrices are similar.
+    eigvals = [operator.eigvals() for operator in self.operators]
+    # Now compute the kronecker product
+    product = eigvals[0]
+    for eigval in eigvals[1:]:
+      # Product has shape [B, R1, 1].
+      product = product[..., array_ops.newaxis]
+      # Eigval has shape [B, 1, R2]. Produces shape [B, R1, R2].
+      product *= eigval[..., array_ops.newaxis, :]
+      # Reshape to [B, R1 * R2]
+      product = array_ops.reshape(
+          product,
+          shape=array_ops.concat([array_ops.shape(product)[:-2], [-1]], axis=0))
+    product.set_shape(self.shape[:-1])
+    return product
+
   def _assert_non_singular(self):
     if all(operator.is_square for operator in self.operators):
       asserts = [operator.assert_non_singular() for operator in self.operators]

tensorflow/python/ops/linalg/linear_operator_lower_triangular.py

@@ -202,3 +202,6 @@ class LinearOperatorLowerTriangular(linear_operator.LinearOperator):
 
   def _to_dense(self):
     return self._get_tril()
+
+  def _eigvals(self):
+    return self._get_diag()

tensorflow/python/ops/linalg/linear_operator_test_util.py

@@ -35,6 +35,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import sort_ops
 from tensorflow.python.ops.linalg import linalg_impl as linalg
 from tensorflow.python.ops.linalg import linear_operator_util
 from tensorflow.python.platform import test
@@ -413,6 +414,32 @@ def _test_cholesky(use_placeholder, shapes_info, dtype):
   return test_cholesky
 
 
+def _test_eigvalsh(use_placeholder, shapes_info, dtype):
+  def test_eigvalsh(self):
+    with self.test_session(graph=ops.Graph()) as sess:
+      sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
+      operator, mat = self.operator_and_matrix(
+          shapes_info, dtype, use_placeholder=use_placeholder,
+          ensure_self_adjoint_and_pd=True)
+      # Eigenvalues are real, so we'll cast these to float64 and sort
+      # for comparison.
+      op_eigvals = sort_ops.sort(
+          math_ops.cast(operator.eigvals(), dtype=dtypes.float64), axis=-1)
+      mat_eigvals = sort_ops.sort(
+          math_ops.cast(
+              linalg_ops.self_adjoint_eigvals(mat), dtype=dtypes.float64),
+          axis=-1)
+      op_eigvals_v, mat_eigvals_v = sess.run([op_eigvals, mat_eigvals])
+
+      atol = self._atol[dtype]  # pylint: disable=protected-access
+      rtol = self._rtol[dtype]  # pylint: disable=protected-access
+      if dtype == dtypes.float32 or dtype == dtypes.complex64:
+        atol = 1e-5
+        rtol = 1e-5
+      self.assertAllClose(op_eigvals_v, mat_eigvals_v, atol=atol, rtol=rtol)
+  return test_eigvalsh
+
+
 def _test_solve_base(
     self,
     use_placeholder,
@@ -552,6 +579,7 @@ def add_tests(test_cls):
       "cholesky": _test_cholesky,
       "det": _test_det,
       "diag_part": _test_diag_part,
+      "eigvalsh": _test_eigvalsh,
       "inverse": _test_inverse,
       "log_abs_det": _test_log_abs_det,
       "matmul": _test_matmul,
@@ -678,6 +706,7 @@ class NonSquareLinearOperatorDerivedClassTest(LinearOperatorDerivedClassTest):
     """List of test names to skip."""
     return [
         "cholesky",
+        "eigvalsh",
         "inverse",
         "solve",
         "solve_with_broadcast",

tensorflow/python/ops/linalg/linear_operator_zeros.py

@@ -454,3 +454,6 @@ class LinearOperatorZeros(linear_operator.LinearOperator):
            [self._min_matrix_dim_tensor()]], axis=0)
 
     return array_ops.zeros(shape=d_shape, dtype=self.dtype)
+
+  def _eigvals(self):
+    return self._zeros_diag()

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-adjoint.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-block-diag.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-circulant.pbtxt

@@ -143,6 +143,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-circulant2-d.pbtxt

@@ -143,6 +143,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-circulant3-d.pbtxt

@@ -143,6 +143,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-composition.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-diag.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-full-matrix.pbtxt

@@ -118,6 +118,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-householder.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-identity.pbtxt

@@ -119,6 +119,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-inversion.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-kronecker.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-low-rank-update.pbtxt

@@ -142,6 +142,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-lower-triangular.pbtxt

@@ -118,6 +118,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-permutation.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-scaled-identity.pbtxt

@@ -123,6 +123,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-toeplitz.pbtxt

@@ -126,6 +126,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator-zeros.pbtxt

@@ -118,6 +118,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v1/tensorflow.linalg.-linear-operator.pbtxt

@@ -117,6 +117,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-adjoint.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-block-diag.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.pbtxt

@@ -143,6 +143,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.pbtxt

@@ -143,6 +143,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.pbtxt

@@ -143,6 +143,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.pbtxt

@@ -118,6 +118,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-householder.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.pbtxt

@@ -119,6 +119,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-inversion.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.pbtxt

@@ -142,6 +142,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.pbtxt

@@ -118,6 +118,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-permutation.pbtxt

@@ -122,6 +122,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.pbtxt

@@ -123,6 +123,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-toeplitz.pbtxt

@@ -126,6 +126,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.pbtxt

@@ -118,6 +118,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "

tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.pbtxt

@@ -117,6 +117,10 @@ tf_class {
     name: "domain_dimension_tensor"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
   }
+  member_method {
+    name: "eigvals"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'eigvals\'], "
+  }
   member_method {
     name: "inverse"
     argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'inverse\'], "