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Use same algorithm to solve 2x2 eigenvalue problem

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The eigenvalue problem is solved in two different method that use different methods
to calculate the discriminant of the solution to the quadratic equation.
Use the method whose computation is considered more stable.
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daingun 2019-11-01 23:27:08 +01:00 committed by GitHub
parent ead2360f8e
commit 640b008fa5
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1 changed files with 7 additions and 7 deletions
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14
src/linalg/schur.rs
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@ -309,16 +309,17 @@ where
let hmn = t[(m, n)];
let hnn = t[(n, n)];
let tra = hnn + hmm;
let det = hnn * hmm - hnm * hmn;
let discr = tra * tra * crate::convert(0.25) - det;
// NOTE: use the same algorithm as in compute_2x2_eigvals.
let val = (hmm - hnn) * crate::convert(0.5);
let discr = hnm * hmn + val * val;
// All 2x2 blocks have negative discriminant because we already decoupled those
// with positive eigenvalues..
// with positive eigenvalues.
let sqrt_discr = NumComplex::new(N::zero(), (-discr).sqrt());
out[m] = NumComplex::new(tra * crate::convert(0.5), N::zero()) + sqrt_discr;
out[m + 1] = NumComplex::new(tra * crate::convert(0.5), N::zero()) - sqrt_discr;
let half_tra = (hnn + hmm) * crate::convert(0.5);
out[m] = NumComplex::new(half_tra, N::zero()) + sqrt_discr;
out[m + 1] = NumComplex::new(half_tra, N::zero()) - sqrt_discr;
m += 2;
}
@ -413,7 +414,6 @@ where
let inv_rot = rot.inverse();
inv_rot.rotate(&mut m);
rot.rotate_rows(&mut m);
m[(1, 0)] = N::zero();
if compute_q {
// XXX: we have to build the matrix manually because