1 | initial version |
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()
2 | No.2 Revision |
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.
3 | No.3 Revision |
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.
4 | No.4 Revision |
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