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BUG: test failure in test_x0_equals_Mb with bicgstab #15533

@h-vetinari

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@h-vetinari

In conda-forge, we ran into new test failure for scipy 1.8, which appears only with MKL (which is available in cf only for x86), and only if the processor supports AVX512 (which azure CI only has for linux & windows), see conda-forge/scipy-feedstock#199.

To not further delay the release of 1.8, we skipped that test for now, but it should IMO be fixed, especially as such processors are becoming more and more common (in fact, it's getting harder and harder to purposefully catch a non-AVX512 windows CI agent on azure)

The failure is in test_x0_equals_Mb[bicgstab] and looks as follows:

=================================== FAILURES ===================================
_________________________ test_x0_equals_Mb[bicgstab] __________________________
[...]/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/tests/test_iterative.py:538: in test_x0_equals_Mb
    assert_equal(info, 0)
E   AssertionError: 
E   Items are not equal:
E    ACTUAL: -11
E    DESIRED: 0
        A          = <10x10 sparse matrix of type '<class 'numpy.complex64'>'
	with 19 stored elements in Compressed Sparse Row format>
        b          = array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
        case       = <nonsymposdef>
        info       = -11
        solver     = <function bicgstab at 0x7fc6545a4ee0>
        sup        = <numpy.testing._private.utils.suppress_warnings object at 0x7fc62555d040>
        tol        = 1e-08
        x          = array([0.        +0.j, 0.50000358+0.j, 1.25016941+0.j, 2.12653797+0.j,
       3.0674186 +0.j, 4.03716976+0.j, 5.0158146 +0.j, 6.00288608+0.j,
       7.        +0.j, 8.        +0.j])
        x0         = 'Mb'

Also, at the end of the test suite, some (seemingly delayed) log output appears that is perhaps relevant:


   Normal return from subroutine COBYLA

   NFVALS =   50   F = 2.485185E+01    MAXCV = 1.999965E-10
   X = 4.955358E+00   6.666553E-01

 NNLS quitting on iteration count.

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    defectA clear bug or issue that prevents SciPy from being installed or used as expectedscipy.sparse.linalg

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