|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/linalg/__init__.py) | Experimental tools for linear algebra. ## Classes [`class LinearOperatorInterpolatedPSDKernel`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/linalg/LinearOperatorInterpolatedPSDKernel): Structured interpolation to approximate a large matrix. [`class LinearOperatorPSDKernel`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/linalg/LinearOperatorPSDKernel): A [`tf.linalg.LinearOperator`](https://www.tensorflow.org/api_docs/python/tf/linalg/LinearOperator) representing a kernel covariance matrix. [`class LinearOperatorRowBlock`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/linalg/LinearOperatorRowBlock): Represents a single row of a block matrix. [`class LinearOperatorUnitary`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/linalg/LinearOperatorUnitary): Encapsulates a Unitary Linear Operator. ## Functions [`no_pivot_ldl(...)`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/linalg/no_pivot_ldl): Non-pivoted batched LDL factorization. [`simple_robustified_cholesky(...)`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/linalg/simple_robustified_cholesky): Use `no_pivot_ldl` to robustify a Cholesky factorization.