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Method cholesky

tensorflow/python/ops/linalg/linear_operator.py:915–938  ·  view source on GitHub ↗

Returns a Cholesky factor as a `LinearOperator`. Given `A` representing this `LinearOperator`, if `A` is positive definite self-adjoint, return `L`, where `A = L L^T`, i.e. the cholesky decomposition. Args: name: A name for this `Op`. Returns: `LinearOperator` whi

(self, name="cholesky")

Source from the content-addressed store, hash-verified

913 return linear_operator_algebra.inverse(self)
914
915 def cholesky(self, name="cholesky"):
916 """Returns a Cholesky factor as a `LinearOperator`.
917
918 Given `A` representing this `LinearOperator`, if `A` is positive definite
919 self-adjoint, return `L`, where `A = L L^T`, i.e. the cholesky
920 decomposition.
921
922 Args:
923 name: A name for this `Op`.
924
925 Returns:
926 `LinearOperator` which represents the lower triangular matrix
927 in the Cholesky decomposition.
928
929 Raises:
930 ValueError: When the `LinearOperator` is not hinted to be positive
931 definite and self adjoint.
932 """
933
934 if not self._can_use_cholesky():
935 raise ValueError("Cannot take the Cholesky decomposition: "
936 "Not a positive definite self adjoint matrix.")
937 with self._name_scope(name):
938 return linear_operator_algebra.cholesky(self)
939
940 def _to_dense(self):
941 """Generic and often inefficient implementation. Override often."""

Callers 15

create_distributionMethod · 0.80
cholFunction · 0.80
testKLNonBatchMethod · 0.80
testKLBatchMethod · 0.80
testKLBatchBroadcastMethod · 0.80
__init__Method · 0.80
_log_probMethod · 0.80
_varianceMethod · 0.80
_stddevMethod · 0.80
__init__Method · 0.80

Calls 2

_can_use_choleskyMethod · 0.95
_name_scopeMethod · 0.95

Tested by 15

create_distributionMethod · 0.64
cholFunction · 0.64
testKLNonBatchMethod · 0.64
testKLBatchMethod · 0.64
testKLBatchBroadcastMethod · 0.64
_verifyCholeskyMethod · 0.64
testNonSquareMatrixMethod · 0.64
testWrongDimensionsMethod · 0.64
runFiniteDifferencesMethod · 0.64
benchmarkCholeskyOpMethod · 0.64