# tfp.math.pinv

Compute the Moore-Penrose pseudo-inverse of a matrix. (deprecated)

``````tfp.math.pinv(
*args,
**kwargs
)
``````

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`.

#### Args:

• `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'.

#### Returns:

• `a_pinv`: The pseudo-inverse of input `a`. Has same shape as `a` except rightmost two dimensions are transposed.

#### Raises:

• `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(tfp.math.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(tfp.math.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.