# dask.array.linalg.svd

### dask.array.linalg.svd(a, coerce_signs=True, full_matrices=False)

Compute the singular value decomposition of a matrix.

* **Parameters:**
  **a**

  **coerce_signs**
  : Whether or not to apply sign coercion to singular vectors in
    order to maintain deterministic results, by default True.

  **full_matrices**
  : If True, raises `NotImplementedError`. Only `full_matrices=False`
    (reduced SVD) is currently supported. Default is `False`.
    <br/>
    #### Versionadded
    Added in version 2026.3.0.
* **Returns:**
  **u**
  : Left-singular vectors of a (in columns) with shape (M, K)
    where K = min(M, N).

  **s**
  : Singular values of a.

  **v**
  : Right-singular vectors of a (in rows) with shape (K, N)
    where K = min(M, N).

#### WARNING
SVD is only supported for arrays with chunking in one dimension.
This requires that all inputs either contain a single column
of chunks (tall-and-skinny) or a single row of chunks (short-and-fat).
For arrays with chunking in both dimensions, see da.linalg.svd_compressed.

#### SEE ALSO
`np.linalg.svd`
: Equivalent NumPy Operation

`da.linalg.svd_compressed`
: Randomized SVD for fully chunked arrays

[`dask.array.linalg.tsqr`](dask.array.linalg.tsqr.html.md#dask.array.linalg.tsqr)
: QR factorization for tall-and-skinny arrays

`dask.array.utils.svd_flip`
: Sign normalization for singular vectors

### Examples

```pycon
>>> u, s, v = da.linalg.svd(x)
```

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