# numpy.linalg.svd¶

`numpy.linalg.``svd`(a, full_matrices=1, compute_uv=1)[source]

Singular Value Decomposition.

Factors the matrix a as `u * np.diag(s) * v`, where u and v are unitary and s is a 1-d array of a‘s singular values.

Parameters: a : (..., M, N) array_like A real or complex matrix of shape (M, N) . full_matrices : bool, optional If True (default), u and v have the shapes (M, M) and (N, N), respectively. Otherwise, the shapes are (M, K) and (K, N), respectively, where K = min(M, N). compute_uv : bool, optional Whether or not to compute u and v in addition to s. True by default. u : { (..., M, M), (..., M, K) } array Unitary matrices. The actual shape depends on the value of `full_matrices`. Only returned when `compute_uv` is True. s : (..., K) array The singular values for every matrix, sorted in descending order. v : { (..., N, N), (..., K, N) } array Unitary matrices. The actual shape depends on the value of `full_matrices`. Only returned when `compute_uv` is True. LinAlgError If SVD computation does not converge.

Notes

New in version 1.8.0.

Broadcasting rules apply, see the `numpy.linalg` documentation for details.

The decomposition is performed using LAPACK routine _gesdd

The SVD is commonly written as `a = U S V.H`. The v returned by this function is `V.H` and `u = U`.

If `U` is a unitary matrix, it means that it satisfies `U.H = inv(U)`.

The rows of v are the eigenvectors of `a.H a`. The columns of u are the eigenvectors of `a a.H`. For row `i` in v and column `i` in u, the corresponding eigenvalue is `s[i]**2`.

If a is a matrix object (as opposed to an ndarray), then so are all the return values.

Examples

```>>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
```

Reconstruction based on full SVD:

```>>> U, s, V = np.linalg.svd(a, full_matrices=True)
>>> U.shape, V.shape, s.shape
((9, 9), (6, 6), (6,))
>>> S = np.zeros((9, 6), dtype=complex)
>>> S[:6, :6] = np.diag(s)
>>> np.allclose(a, np.dot(U, np.dot(S, V)))
True
```

Reconstruction based on reduced SVD:

```>>> U, s, V = np.linalg.svd(a, full_matrices=False)
>>> U.shape, V.shape, s.shape
((9, 6), (6, 6), (6,))
>>> S = np.diag(s)
>>> np.allclose(a, np.dot(U, np.dot(S, V)))
True
```

numpy.linalg.qr

numpy.linalg.eig