tf.linalg.set

Solves systems of linear equations.

View aliases

Compat aliases for migration

tf.compat.v1.linalg.solve, tf.compat.v1.matrix_solve

tf.linalg.solve(
    matrix, rhs, adjoint=False, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Advanced Automatic Differentiation	A Tour of TensorFlow Probability

Matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. Rhs is a tensor of shape [..., M, K]. The output is a tensor shape [..., M, K]. If adjoint is False then each output matrix satisfies matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]. If adjoint is True then each output matrix satisfies adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :].

Args
`matrix`	A `Tensor`. Must be one of the following types: `float64`, `float32`, `half`, `complex64`, `complex128`. Shape is `[..., M, M]`.
`rhs`	A `Tensor`. Must have the same type as `matrix`. Shape is `[..., M, K]`.
`adjoint`	An optional `bool`. Defaults to `False`. Boolean indicating whether to solve with `matrix` or its (block-wise) adjoint.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `matrix`.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.linalg / set_diag

View aliases

Used in the notebooks

Args

Returns