# Revision history [back]

We have to inspect further for the reasons of that behaviour. It might "only" be the naiveness of the algorithm, since the matrix B is ill-conditionned: its inverse has a huge norm.

I bet there is something wrong in the formulas, since when i do the computation on certified fields such as RBF and RIF, the correct answer does not belong to the possible values provided by Sage.

Meanwhile, you can always turn your matrix into a dense one by doing:

sage: B.dense_matrix()


We have to inspect further for the reasons of that behaviour. It might "only" be the naiveness of the algorithm, since the matrix B is ill-conditionned: its inverse has a huge norm.

I bet there is something wrong in the formulas, since when i do the computation on certified fields such as RBF and RIF, the correct answer does not belong to the possible values provided by Sage.

Meanwhile, you can always turn your matrix into a dense one by doing:

sage: B.dense_matrix()


Thanks for reporting anyway.

We have to inspect further for the reasons of that behaviour. It might "only" be the naiveness of the algorithm, since the matrix B is ill-conditionned: its inverse has a huge norm.

I bet there is something wrong in the formulas, since when i do the computation on certified fields such as RBF and RIF, the correct answer does not belong to the possible values provided by Sage.

Meanwhile, you can always turn your matrix into a dense one by doing:

sage: B.dense_matrix()


Thanks for reporting anyway.

EDIT The culprit is https://trac.sagemath.org/ticket/24122 if you revert it, you get something close to 1 again.

We have to inspect further for the reasons of that behaviour. It might "only" be the naiveness of the algorithm, since the matrix B is ill-conditionned: its inverse has a huge norm.

I bet there is something wrong in the formulas, since when i do the computation on certified fields such as RBF and RIF, the correct answer does not belong to the possible values provided by Sage.

Meanwhile, you can always turn your matrix into a dense one by doing:

sage: B.dense_matrix()


Thanks for reporting anyway.

EDIT The culprit is https://trac.sagemath.org/ticket/24122 seems to be trac ticket 24122 if you revert it, you get something close to 1 again.

This bug is tracked at trac ticket 28402