tf.linalg.pinv

Compute the Moore-Penrose pseudo-inverse of one or more matrices.

tf.linalg.pinv(     a, rcond=None, validate_args=False, name=None )

Calculate the generalized inverse of a matrix using its singular-value decomposition (SVD) and including all large singular values.

The pseudo-inverse of a matrix A, is defined as: 'the matrix that 'solves' [the least-squares problem] A @ x = b,' i.e., if x_hat is a solution, then A_pinv is the matrix such that x_hat = A_pinv @ b. It can be shown that if U @ Sigma @ V.T = A is the singular value decomposition of A, then A_pinv = V @ inv(Sigma) U^T. [(Strang, 1980)][1]

This function is analogous to numpy.linalg.pinv. It differs only in default value of rcond. In numpy.linalg.pinv, the default rcond is 1e-15. Here the default is 10. * max(num_rows, num_cols) * np.finfo(dtype).eps.

a (Batch of) float-like matrix-shaped Tensor(s) which are to be pseudo-inverted.
rcond Tensor of small singular value cutoffs. Singular values smaller (in modulus) than rcond * largest_singular_value (again, in modulus) are set to zero. Must broadcast against tf.shape(a)[:-2]. Default value: 10. * max(num_rows, num_cols) * np.finfo(a.dtype).eps.
validate_args When True, additional assertions might be embedded in the graph. Default value: False (i.e., no graph assertions are added).
name Python str prefixed to ops created by this function. Default value: 'pinv'.

a_pinv (Batch of) pseudo-inverse of input a. Has same shape as a except rightmost two dimensions are transposed.

TypeError if input a does not have float-like dtype.
ValueError if input a has fewer than 2 dimensions.

Examples

import tensorflow as tf import tensorflow_probability as tfp  a = tf.constant([[1.,  0.4,  0.5],                  [0.4, 0.2,  0.25],                  [0.5, 0.25, 0.35]]) tf.matmul(tf.linalg.pinv(a), a) # ==> array([[1., 0., 0.],              [0., 1., 0.],              [0., 0., 1.]], dtype=float32)  a = tf.constant([[1.,  0.4,  0.5,  1.],                  [0.4, 0.2,  0.25, 2.],                  [0.5, 0.25, 0.35, 3.]]) tf.matmul(tf.linalg.pinv(a), a) # ==> array([[ 0.76,  0.37,  0.21, -0.02],              [ 0.37,  0.43, -0.33,  0.02],              [ 0.21, -0.33,  0.81,  0.01],              [-0.02,  0.02,  0.01,  1.  ]], dtype=float32)

References

[1]: G. Strang. 'Linear Algebra and Its Applications, 2nd Ed.' Academic Press, Inc., 1980, pp. 139-142.