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* bug fixes in: Dot, generalized eigen problem, singular matrix detetection in Cholesky
* fix all numerical instabilies in the unit tests, now all tests can be run 2000 times with almost zero failures.
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@@ -93,17 +93,18 @@ void Cholesky<MatrixType>::compute(const MatrixType& a)
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assert(a.rows()==a.cols());
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const int size = a.rows();
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m_matrix.resize(size, size);
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const RealScalar eps = ei_sqrt(precision<Scalar>());
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RealScalar x;
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x = ei_real(a.coeff(0,0));
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m_isPositiveDefinite = x > precision<Scalar>() && ei_isMuchSmallerThan(ei_imag(a.coeff(0,0)), RealScalar(1));
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m_isPositiveDefinite = x > eps && ei_isMuchSmallerThan(ei_imag(a.coeff(0,0)), RealScalar(1));
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m_matrix.coeffRef(0,0) = ei_sqrt(x);
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m_matrix.col(0).end(size-1) = a.row(0).end(size-1).adjoint() / ei_real(m_matrix.coeff(0,0));
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for (int j = 1; j < size; ++j)
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{
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Scalar tmp = ei_real(a.coeff(j,j)) - m_matrix.row(j).start(j).norm2();
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x = ei_real(tmp);
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if (x < precision<Scalar>() || (!ei_isMuchSmallerThan(ei_imag(tmp), RealScalar(1))))
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if (x < eps || (!ei_isMuchSmallerThan(ei_imag(tmp), RealScalar(1))))
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{
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m_isPositiveDefinite = false;
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return;
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@@ -94,6 +94,7 @@ void CholeskyWithoutSquareRoot<MatrixType>::compute(const MatrixType& a)
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const int size = a.rows();
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m_matrix.resize(size, size);
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m_isPositiveDefinite = true;
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const RealScalar eps = ei_sqrt(precision<Scalar>());
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// Let's preallocate a temporay vector to evaluate the matrix-vector product into it.
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// Unlike the standard Cholesky decomposition, here we cannot evaluate it to the destination
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@@ -111,7 +112,7 @@ void CholeskyWithoutSquareRoot<MatrixType>::compute(const MatrixType& a)
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RealScalar tmp = ei_real(a.coeff(j,j) - (m_matrix.row(j).start(j) * m_matrix.col(j).start(j).conjugate()).coeff(0,0));
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m_matrix.coeffRef(j,j) = tmp;
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if (ei_isMuchSmallerThan(tmp,RealScalar(1)))
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if (tmp < eps)
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{
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m_isPositiveDefinite = false;
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return;
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