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scipy.sparse.linalg.

# LinearOperator

class scipy.sparse.linalg.LinearOperator(*args, **kwargs)

  Common interface for performing matrix vector products

  Many iterative methods (e.g. `cg`, `gmres`) do not need to know the individual entries of a matrix to solve a linear system `A@x = b`. Such solvers only require the computation of matrix vector products, `A@v` where `v` is a dense vector. This class serves as an abstract interface between iterative solvers and matrix-like objects.

  To construct a concrete `LinearOperator`, either pass appropriate callables to the constructor of this class, or subclass it.

  A subclass must implement either one of the methods `_matvec` and `_matmat`, and the attributes/properties `shape` (pair of integers) and `dtype` (may be None). It may call the `__init__` on this class to have these attributes validated. Implementing `_matvec` automatically implements `_matmat` (using a naive algorithm) and vice-versa.

  Optionally, a subclass may implement `_rmatvec` or `_adjoint` to implement the Hermitian adjoint (conjugate transpose). As with `_matvec` and `_matmat`, implementing either `_rmatvec` or `_adjoint` implements the other automatically. Implementing `_adjoint` is preferable; `_rmatvec` is mostly there for backwards compatibility.

  Parameters:
    shapetuple

      Matrix dimensions `(M, N)`.

    matveccallable f(v)

      Returns returns `A @ v`.

    rmatveccallable f(v)

      Returns `A^H @ v`, where `A^H` is the conjugate transpose of `A`.

    matmatcallable f(V)

      Returns `A @ V`, where `V` is a dense matrix with dimensions `(N, K)`.

    dtypedtype

      Data type of the matrix.

    rmatmatcallable f(V)

      Returns `A^H @ V`, where `V` is a dense matrix with dimensions `(M, K)`.

  Attributes:
    argstuple

      For linear operators describing products etc. of other linear operators, the operands of the binary operation.

    ndimint

      Number of dimensions (this is always 2)

  Methods

  `__call__`(x) | Call self as a function.
  `adjoint`() | Hermitian adjoint.
  `dot`(x) | Matrix-matrix or matrix-vector multiplication.
  `matmat`(X) | Matrix-matrix multiplication.
  `matvec`(x) | Matrix-vector multiplication.
  `rmatmat`(X) | Adjoint matrix-matrix multiplication.
  `rmatvec`(x) | Adjoint matrix-vector multiplication.
  `transpose`() | Transpose this linear operator.

  __mul__ |

  See also

  `aslinearoperator`

    Construct LinearOperators

  Notes

  The user-defined `matvec` function must properly handle the case where `v` has shape `(N,)` as well as the `(N,1)` case. The shape of the return type is handled internally by `LinearOperator`.

  It is highly recommended to explicitly specify the dtype, otherwise it is determined automatically at the cost of a single matvec application on `int8` zero vector using the promoted dtype of the output. Python `int` could be difficult to automatically cast to numpy integers in the definition of the `matvec` so the determination may be inaccurate. It is assumed that `matmat`, `rmatvec`, and `rmatmat` would result in the same dtype of the output given an `int8` input as `matvec`.

  LinearOperator instances can also be multiplied, added with each other and exponentiated, all lazily: the result of these operations is always a new, composite LinearOperator, that defers linear operations to the original operators and combines the results.

  More details regarding how to subclass a LinearOperator and several examples of concrete LinearOperator instances can be found in the external project PyLops.

  Examples

      >>> import numpy as np
      >>> from scipy.sparse.linalg import LinearOperator
      >>> def mv(v):
      ...     return np.array([2*v[0], 3*v[1]])
      ...
      >>> A = LinearOperator((2,2), matvec=mv)
      >>> A
      <2x2 _CustomLinearOperator with dtype=int8>
      >>> A.matvec(np.ones(2))
      array([ 2.,  3.])
      >>> A @ np.ones(2)
      array([ 2.,  3.])