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# scipy.sparse.linalg.lsmr

scipy.sparse.linalg.lsmr(A, b, damp=0.0, atol=1e-06, btol=1e-06, conlim=100000000.0, maxiter=None, show=False, x0=None)

  Iterative solver for least-squares problems.

  lsmr solves the system of linear equations `Ax = b`. If the system is inconsistent, it solves the least-squares problem `min ||b - Ax||_2`. `A` is a rectangular matrix of dimension m-by-n, where all cases are allowed: m = n, m > n, or m < n. `b` is a vector of length m. The matrix A may be dense or sparse (usually sparse).

  Parameters:
    A{sparse matrix, ndarray, LinearOperator}

      Matrix A in the linear system. Alternatively, `A` can be a linear operator which can produce `Ax` and `A^H x` using, e.g., `scipy.sparse.linalg.LinearOperator`.

    b array_like, shape (m,)

      Vector `b` in the linear system.

    damp float

      Damping factor for regularized least-squares. `lsmr` solves the regularized least-squares problem:

          min ||(b) - (  A   )x||
              ||(0)   (damp*I) ||_2

      where damp is a scalar. If damp is None or 0, the system is solved without regularization. Default is 0.

    atol, btol float, optional

      Stopping tolerances. `lsmr` continues iterations until a certain backward error estimate is smaller than some quantity depending on atol and btol. Let `r = b - Ax` be the residual vector for the current approximate solution `x`. If `Ax = b` seems to be consistent, `lsmr` terminates when `norm(r) <= atol * norm(A) * norm(x) + btol * norm(b)`. Otherwise, `lsmr` terminates when `norm(A^H r) <= atol * norm(A) * norm(r)`. If both tolerances are 1.0e-6 (default), the final `norm(r)` should be accurate to about 6 digits. (The final `x` will usually have fewer correct digits, depending on `cond(A)` and the size of LAMBDA.) If atol or btol is None, a default value of 1.0e-6 will be used. Ideally, they should be estimates of the relative error in the entries of `A` and `b` respectively. For example, if the entries of `A` have 7 correct digits, set `atol = 1e-7`. This prevents the algorithm from doing unnecessary work beyond the uncertainty of the input data.

    conlim float, optional

      `lsmr` terminates if an estimate of `cond(A)` exceeds conlim. For compatible systems `Ax = b`, conlim could be as large as 1.0e+12 (say). For least-squares problems, conlim should be less than 1.0e+8. If conlim is None, the default value is 1e+8. Maximum precision can be obtained by setting `atol = btol = conlim = 0`, but the number of iterations may then be excessive. Default is 1e8.

    maxiter int, optional

      `lsmr` terminates if the number of iterations reaches maxiter. The default is `maxiter = min(m, n)`. For ill-conditioned systems, a larger value of maxiter may be needed. Default is False.

    show bool, optional

      Print iterations logs if `show=True`. Default is False.

    x0 array_like, shape (n,), optional

      Initial guess of `x`, if None zeros are used. Default is None.

      New in version 1.0.0.

  Returns:
    x ndarray of float

      Least-square solution returned.

    istop int

      istop gives the reason for stopping:

          istop   = 0 means x=0 is a solution.  If x0 was given, then x=x0 is a
                      solution.
                  = 1 means x is an approximate solution to A@x = B,
                      according to atol and btol.
                  = 2 means x approximately solves the least-squares problem
                      according to atol.
                  = 3 means COND(A) seems to be greater than CONLIM.
                  = 4 is the same as 1 with atol = btol = eps (machine
                      precision)
                  = 5 is the same as 2 with atol = eps.
                  = 6 is the same as 3 with CONLIM = 1/eps.
                  = 7 means ITN reached maxiter before the other stopping
                      conditions were satisfied.

    itn int

      Number of iterations used.

    normr float

      `norm(b-Ax)`

    normar float

      `norm(A^H (b - Ax))`

    norma float

      `norm(A)`

    conda float

      Condition number of A.

    normx float

      `norm(x)`

  Notes

  New in version 0.11.0.

  References

  [1]

  D. C.-L. Fong and M. A. Saunders, “LSMR: An iterative algorithm for sparse least-squares problems”, SIAM J. Sci. Comput., vol. 33, pp. 2950-2971, 2011. arXiv:1006.0758

  [2]

  LSMR Software, https://web.stanford.edu/group/SOL/software/lsmr/

  Examples

      >>> import numpy as np
      >>> from scipy.sparse import csc_matrix
      >>> from scipy.sparse.linalg import lsmr
      >>> A = csc_matrix([[1., 0.], [1., 1.], [0., 1.]], dtype=float)

  The first example has the trivial solution `[0, 0]`

      >>> b = np.array([0., 0., 0.], dtype=float)
      >>> x, istop, itn, normr = lsmr(A, b)[:4]
      >>> istop
      0
      >>> x
      array([0., 0.])

  The stopping code istop=0 returned indicates that a vector of zeros was found as a solution. The returned solution x indeed contains `[0., 0.]`. The next example has a non-trivial solution:

      >>> b = np.array([1., 0., -1.], dtype=float)
      >>> x, istop, itn, normr = lsmr(A, b)[:4]
      >>> istop
      1
      >>> x
      array([ 1., -1.])
      >>> itn
      1
      >>> normr
      4.440892098500627e-16

  As indicated by istop=1, `lsmr` found a solution obeying the tolerance limits. The given solution `[1., -1.]` obviously solves the equation. The remaining return values include information about the number of iterations (itn=1) and the remaining difference of left and right side of the solved equation. The final example demonstrates the behavior in the case where there is no solution for the equation:

      >>> b = np.array([1., 0.01, -1.], dtype=float)
      >>> x, istop, itn, normr = lsmr(A, b)[:4]
      >>> istop
      2
      >>> x
      array([ 1.00333333, -0.99666667])
      >>> A.dot(x)-b
      array([ 0.00333333, -0.00333333,  0.00333333])
      >>> normr
      0.005773502691896255

  istop indicates that the system is inconsistent and thus x is rather an approximate solution to the corresponding least-squares problem. normr contains the minimal distance that was found.