{
  "__type": "IngestedDoc",
  "__tag": 4010,
  "_content": {
    "Notes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "When "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "compute_uv"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is True, the result is a namedtuple with the following attribute names: "
            },
            {
              "__type": "CrossRef",
              "__tag": 4002,
              "value": "U",
              "reference": {
                "__type": "RefInfo",
                "__tag": 4000,
                "module": null,
                "version": null,
                "kind": "local",
                "path": "U"
              },
              "kind": "local"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", "
            },
            {
              "__type": "CrossRef",
              "__tag": 4002,
              "value": "S",
              "reference": {
                "__type": "RefInfo",
                "__tag": 4000,
                "module": null,
                "version": null,
                "kind": "local",
                "path": "S"
              },
              "kind": "local"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", and "
            },
            {
              "__type": "CrossRef",
              "__tag": 4002,
              "value": "Vh",
              "reference": {
                "__type": "RefInfo",
                "__tag": 4000,
                "module": null,
                "version": null,
                "kind": "local",
                "path": "Vh"
              },
              "kind": "local"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "The decomposition is performed using LAPACK routine "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "_gesdd"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "SVD is usually described for the factorization of a 2D matrix "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "A"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". The higher-dimensional case will be discussed below. In the 2D case, SVD is written as "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "A = U S V^H"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", where "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "A = a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "U= u"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "S= \\mathtt{np.diag}(s)"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "V^H = vh"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". The 1D array "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "s",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " contains the singular values of "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "u",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "vh",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " are unitary. The rows of "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "vh",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " are the eigenvectors of "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "A^H A"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and the columns of "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "u",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " are the eigenvectors of "
            },
            {
              "__type": "InlineMath",
              "__tag": 4057,
              "value": "A A^H"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". In both cases the corresponding (possibly non-zero) eigenvalues are given by "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "s**2"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "If "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " has more than two dimensions, then broadcasting rules apply, as explained in "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "routines.linalg-broadcasting",
              "domain": null,
              "role": "ref",
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". This means that SVD is working in \"stacked\" mode: it iterates over all indices of the first "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "a.ndim - 2"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " dimensions and for each combination SVD is applied to the last two indices. The matrix "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " can be reconstructed from the decomposition with either "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "(u * s[..., None, :]) @ vh"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " or "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "u @ (s[..., None] * vh)"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". (The "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "@"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " operator can be replaced by the function "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.matmul"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " for python versions below 3.5.)"
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "If "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is a "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "matrix"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " object (as opposed to an "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "), then so are all the return values."
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Warns": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Raises": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "",
              "annotation": "LinAlgError",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "If SVD computation does not converge."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Yields": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Methods": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Returns": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "U",
              "annotation": "{ (..., M, M), (..., M, K) } array",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Unitary array(s). The first "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "a.ndim - 2"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " dimensions have the same size as those of the input "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "a"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ". The size of the last two dimensions depends on the value of "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "full_matrices"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ". Only returned when "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "compute_uv"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " is True."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "S",
              "annotation": "(..., K) array",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Vector(s) with the singular values, within each vector sorted in descending order. The first "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "a.ndim - 2"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " dimensions have the same size as those of the input "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "a"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "Vh",
              "annotation": "{ (..., N, N), (..., K, N) } array",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Unitary array(s). The first "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "a.ndim - 2"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " dimensions have the same size as those of the input "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "a"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ". The size of the last two dimensions depends on the value of "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "full_matrices"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ". Only returned when "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "compute_uv"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " is True."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Singular Value Decomposition."
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Receives": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Warnings": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Attributes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Parameters": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "a",
              "annotation": "(..., M, N) array_like",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "A real or complex array with "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "a.ndim >= 2"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "full_matrices",
              "annotation": "bool, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "If True (default), "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "u",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " and "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "vh",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " have the shapes "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "(..., M, M)"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " and "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "(..., N, N)"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", respectively.  Otherwise, the shapes are "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "(..., M, K)"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " and "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "(..., K, N)"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", respectively, where "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "K = min(M, N)"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "compute_uv",
              "annotation": "bool, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Whether or not to compute "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "u",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " and "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "vh",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " in addition to "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "s",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ".  True by default."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "hermitian",
              "annotation": "bool, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "If True, "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "a"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " is assumed to be Hermitian (symmetric if real-valued), enabling a more efficient method for finding singular values. Defaults to False."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Extended Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "When "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is a 2D array, and "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "full_matrices=False"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", then it is factorized as "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "u @ np.diag(s) @ vh = (u * s) @ vh"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", where "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "u",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and the Hermitian transpose of "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "vh",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " are 2D arrays with orthonormal columns and "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "s",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is a 1D array of "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "'s singular values. When "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "a"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is higher-dimensional, SVD is applied in stacked mode as explained below."
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Other Parameters": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    }
  },
  "_ordered_sections": [
    "Summary",
    "Extended Summary",
    "Parameters",
    "Attributes",
    "Methods",
    "Returns",
    "Yields",
    "Receives",
    "Other Parameters",
    "Raises",
    "Warns",
    "Warnings",
    "Notes"
  ],
  "item_file": "/numpy/linalg/_linalg.py",
  "item_line": 1668,
  "item_type": "_ArrayFunctionDispatcher",
  "aliases": [
    "numpy.linalg.svd"
  ],
  "example_section_data": {
    "__type": "Section",
    "__tag": 4015,
    "children": [
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "import numpy as np\nrng = np.random.default_rng()\na = rng.normal(size=(9, 6)) + 1j*rng.normal(size=(9, 6))\nb = rng.normal(size=(2, 7, 8, 3)) + 1j*rng.normal(size=(2, 7, 8, 3))\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nReconstruction based on full SVD, 2D case:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "U, S, Vh = np.linalg.svd(a, full_matrices=True)\nU.shape, S.shape, Vh.shape\nnp.allclose(a, np.dot(U[:, :6] * S, Vh))\nsmat = np.zeros((9, 6), dtype=complex)\nsmat[:6, :6] = np.diag(S)\nnp.allclose(a, np.dot(U, np.dot(smat, Vh)))\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nReconstruction based on reduced SVD, 2D case:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "U, S, Vh = np.linalg.svd(a, full_matrices=False)\nU.shape, S.shape, Vh.shape\nnp.allclose(a, np.dot(U * S, Vh))\nsmat = np.diag(S)\nnp.allclose(a, np.dot(U, np.dot(smat, Vh)))\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nReconstruction based on full SVD, 4D case:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "U, S, Vh = np.linalg.svd(b, full_matrices=True)\nU.shape, S.shape, Vh.shape\nnp.allclose(b, np.matmul(U[..., :3] * S[..., None, :], Vh))\nnp.allclose(b, np.matmul(U[..., :3], S[..., None] * Vh))\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nReconstruction based on reduced SVD, 4D case:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "U, S, Vh = np.linalg.svd(b, full_matrices=False)\nU.shape, S.shape, Vh.shape\nnp.allclose(b, np.matmul(U * S[..., None, :], Vh))\nnp.allclose(b, np.matmul(U, S[..., None] * Vh))\n",
        "execution_status": "success"
      }
    ],
    "title": [],
    "level": 0,
    "target": null
  },
  "see_also": [
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.linalg.svd",
        "reference": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "current-module",
          "version": "current-version",
          "kind": "to-resolve",
          "path": "scipy.linalg.svd"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Similar function in SciPy."
            }
          ]
        }
      ],
      "type": null
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.linalg.svdvals",
        "reference": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "current-module",
          "version": "current-version",
          "kind": "to-resolve",
          "path": "scipy.linalg.svdvals"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Compute singular values of a matrix."
            }
          ]
        }
      ],
      "type": null
    }
  ],
  "signature": {
    "__type": "SignatureNode",
    "__tag": 4029,
    "kind": "function",
    "parameters": [
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "a",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "full_matrices",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "True"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "compute_uv",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "True"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "hermitian",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "False"
      }
    ],
    "return_annotation": {
      "__type": "Empty",
      "__tag": 4031
    },
    "target_name": "svd"
  },
  "references": null,
  "qa": "numpy.linalg:svd",
  "arbitrary": [],
  "local_refs": [
    "S",
    "U",
    "Vh",
    "a",
    "compute_uv",
    "full_matrices",
    "hermitian"
  ]
}