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Fix real schur and polynomial solver.
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@ -408,28 +408,29 @@ inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& ex
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shiftInfo.coeffRef(1) = m_matT.coeff(iu - 1, iu - 1);
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shiftInfo.coeffRef(2) = m_matT.coeff(iu, iu - 1) * m_matT.coeff(iu - 1, iu);
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// Wilkinson's original ad hoc shift
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if (iter == 10) {
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exshift += shiftInfo.coeff(0);
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for (Index i = 0; i <= iu; ++i) m_matT.coeffRef(i, i) -= shiftInfo.coeff(0);
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Scalar s = abs(m_matT.coeff(iu, iu - 1)) + abs(m_matT.coeff(iu - 1, iu - 2));
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shiftInfo.coeffRef(0) = Scalar(0.75) * s;
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shiftInfo.coeffRef(1) = Scalar(0.75) * s;
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shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
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}
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// MATLAB's new ad hoc shift
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if (iter == 30) {
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Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
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s = s * s + shiftInfo.coeff(2);
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if (s > Scalar(0)) {
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s = sqrt(s);
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if (shiftInfo.coeff(1) < shiftInfo.coeff(0)) s = -s;
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s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
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s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
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exshift += s;
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for (Index i = 0; i <= iu; ++i) m_matT.coeffRef(i, i) -= s;
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shiftInfo.setConstant(Scalar(0.964));
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// Alternate exceptional shifting strategy every 16 iterations.
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if (iter % 16 == 0) {
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// Wilkinson's original ad hoc shift
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if (iter % 32 != 0) {
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exshift += shiftInfo.coeff(0);
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for (Index i = 0; i <= iu; ++i) m_matT.coeffRef(i, i) -= shiftInfo.coeff(0);
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Scalar s = abs(m_matT.coeff(iu, iu - 1)) + abs(m_matT.coeff(iu - 1, iu - 2));
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shiftInfo.coeffRef(0) = Scalar(0.75) * s;
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shiftInfo.coeffRef(1) = Scalar(0.75) * s;
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shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
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} else {
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// MATLAB's new ad hoc shift
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Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
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s = s * s + shiftInfo.coeff(2);
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if (s > Scalar(0)) {
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s = sqrt(s);
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if (shiftInfo.coeff(1) < shiftInfo.coeff(0)) s = -s;
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s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
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s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
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exshift += s;
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for (Index i = 0; i <= iu; ++i) m_matT.coeffRef(i, i) -= s;
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shiftInfo.setConstant(Scalar(0.964));
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}
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}
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}
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}
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@ -97,6 +97,13 @@ void schur(int size = MatrixType::ColsAtCompileTime) {
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}
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}
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void test_bug2633() {
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Eigen::MatrixXd A(4, 4);
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A << 0, 0, 0, -2, 1, 0, 0, -0, 0, 1, 0, 2, 0, 0, 2, -0;
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RealSchur<Eigen::MatrixXd> schur(A);
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VERIFY(schur.info() == Eigen::Success);
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}
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EIGEN_DECLARE_TEST(schur_real) {
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CALL_SUBTEST_1((schur<Matrix4f>()));
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CALL_SUBTEST_2((schur<MatrixXd>(internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 4))));
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@ -105,4 +112,6 @@ EIGEN_DECLARE_TEST(schur_real) {
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// Test problem size constructors
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CALL_SUBTEST_5(RealSchur<MatrixXf>(10));
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CALL_SUBTEST_6((test_bug2633()));
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}
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@ -320,6 +320,7 @@ class PolynomialSolver : public PolynomialSolverBase<Scalar_, Deg_> {
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internal::companion<Scalar, Deg_> companion(poly);
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companion.balance();
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m_eigenSolver.compute(companion.denseMatrix());
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eigen_assert(m_eigenSolver.info() == Eigen::Success);
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m_roots = m_eigenSolver.eigenvalues();
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// cleanup noise in imaginary part of real roots:
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// if the imaginary part is rather small compared to the real part
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