Looking at it seems that the improvements were for `is_positive_definite`,
`is_positive_semidefinite` etc rather than `is_indefinite`.
These cases compute fine:
In [*11*]: B1.is_positive_semidefinite
Out[*11*]: False
In [*12*]: B1.is_negative_semidefinite
Out[*12*]: False
I think that should be enough to conclude that the matrix is indefinite.
The improvements are that the algorithms for those cases no longer attempt
to compute the eigenvalues. Computing the eigenvalues symbolically is not
usually possible for bigger than a 4x4 matrix due to Abel-Ruffini:
https://en.wikipedia.org/wiki/Abel%E2%80%93Ruffini_theorem
As a workaround you can convert to a matrix of floats since then the
eigenvalues can be computed approximately by mpmath:
In [*13*]: B1.n().eigenvals()
Out[*13*]: {-0.423518261117994: 1, 0.830838432644968: 1,
1.57353243409785: 1, 2.309362853398: 1, 3.91273545644822: 1,
4.79704908452897: 1}
In [*14*]: B1.n().is_indefinite
Out[*14*]: True
--
Oscar
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