# mxnet.np.linalg.pinv¶

pinv(a, rtol=None, hermitian=False)

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

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

Notes

rtol param is requested in array-api-standard in https://data-apis.org/array-api/latest/extensions/generated/signatures.linalg.pinv.html instead of a parameter in official NumPy operator.

Parameters
• a ((.., M, N) ndarray) – Matrix or stack of matrices to be pseudo-inverted.

• rtol ((..) {float or ndarray of float}, optional) – Cutoff for small singular values. Singular values less than or equal to rtol * largest_singular_value are set to zero. Broadcasts against the stack of matrices.

• hermitian (bool, optional) – If True, a is assumed to be Hermitian (symmetric if real-valued), enabling a more efficient method for finding singular values. Defaults to False.

Returns

B – The pseudo-inverse of a. If a is a matrix instance, then so is B.

Return type

(.., N, M) ndarray

Raises

MXNetError – If the SVD computation does not converge.

Notes

The pseudo-inverse of a matrix A, denoted $$A^+$$, is defined as: “the matrix that ‘solves’ [the least-squares problem] $$Ax = b$$,” i.e., if $$\\bar{x}$$ is said solution, then $$A^+$$ is that matrix such that $$\\bar{x} = A^+b$$.

It can be shown that if $$Q_1 \\Sigma Q_2^T = A$$ is the singular value decomposition of A, then $$A^+ = Q_2 \\Sigma^+ Q_1^T$$, where $$Q_{1,2}$$ are orthogonal matrices, $$\\Sigma$$ is a diagonal matrix consisting of A’s so-called singular values, (followed, typically, by zeros), and then $$\\Sigma^+$$ is simply the diagonal matrix consisting of the reciprocals of A’s singular values (again, followed by zeros). 1

References

1

G. Strang, Linear Algebra and Its Applications, 2nd Ed., Orlando, FL, Academic Press, Inc., 1980, pp. 139-142.

Examples

The following example checks that a * a+ * a == a and a+ * a * a+ == a+: >>> a = np.random.randn(2, 3) >>> pinv_a = np.linalg.pinv(a) >>> (a - np.dot(a, np.dot(pinv_a, a))).sum() array(0.) >>> (pinv_a - np.dot(pinv_a, np.dot(a, pinv_a))).sum() array(0.)