CHOLMOD_factorization_solve_xt_JtJ_bt.docstring

Solves the linear system JtJ x = b using CHOLMOD

SYNOPSIS

    from scipy.sparse import csr_matrix

    indptr  = np.array([0, 2, 3, 6, 8])
    indices = np.array([0, 2, 2, 0, 1, 2, 1, 2])
    data    = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=float)

    Jsparse = csr_matrix((data, indices, indptr))
    Jdense  = Jsparse.toarray()
    print(Jdense)
    ===> [[1. 0. 2.] 
          [0. 0. 3.] 
          [4. 5. 6.] 
          [0. 7. 8.]]

    bt = np.array(((1., 5., 3.), (2., -2., -8)))
    print(nps.transpose(bt))
    ===> [[ 1.  2.] 
          [ 5. -2.] 
          [ 3. -8.]]

    F  = mrcal.CHOLMOD_factorization(Jsparse)
    xt = F.solve_xt_JtJ_bt(bt)
    print(nps.transpose(xt))
    ===> [[ 0.02199662  0.33953751] 
          [ 0.31725888  0.46982516] 
          [-0.21996616 -0.50648618]]

    print(nps.matmult(nps.transpose(Jdense), Jdense, nps.transpose(xt)))
    ===> [[ 1.  2.] 
          [ 5. -2.] 
          [ 3. -8.]]

The core of the mrcal optimizer is a sparse linear least squares solver using
CHOLMOD to solve a large, sparse linear system. CHOLMOD is a C library, but it
is sometimes useful to invoke it from Python.

The CHOLMOD_factorization class factors a matrix JtJ, and this method uses that
factorization to efficiently solve the linear equation JtJ x = b. The usual
linear algebra conventions refer to column vectors, but numpy generally deals
with row vectors, so I talk about solving the equivalent transposed problem: xt
JtJ = bt. The difference is purely notational.

As many vectors b as we'd like may be given at one time (in rows of bt). The
dimensions of the returned array xt will match the dimensions of the given array
bt.

Broadcasting is supported: any leading dimensions will be processed correctly,
as long as bt has shape (..., Nstate)

This function carefully checks its input for validity, but makes no effort to be
flexible: anything that doesn't look right will result in an exception.
Specifically:

- bt must be C-contiguous (the normal numpy order)

- bt must contain 64-bit floating-point values (dtype=float)

ARGUMENTS

- bt: a numpy array of shape (..., Nstate). This array must be C-contiguous and
  it must have dtype=float

RETURNED VALUE

The transpose of the solution array x, in a numpy array of the same shape as the
input bt