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Avoid inefficient 2x2 LU. Move atanh to internal for maintainability.
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@ -519,6 +519,53 @@ inline EIGEN_MATHFUNC_RETVAL(atan2, Scalar) atan2(const Scalar& x, const Scalar&
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return EIGEN_MATHFUNC_IMPL(atan2, Scalar)::run(x, y);
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}
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/****************************************************************************
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* Implementation of atanh2 *
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****************************************************************************/
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template<typename Scalar, bool IsInteger>
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struct atanh2_default_impl
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{
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typedef Scalar retval;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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static inline Scalar run(const Scalar& x, const Scalar& y)
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{
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using std::abs;
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using std::log;
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using std::sqrt;
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Scalar z = x / y;
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if (abs(z) > sqrt(NumTraits<RealScalar>::epsilon()))
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return RealScalar(0.5) * log((y + x) / (y - x));
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else
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return z + z*z*z / RealScalar(3);
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}
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};
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template<typename Scalar>
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struct atanh2_default_impl<Scalar, true>
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{
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static inline Scalar run(const Scalar&, const Scalar&)
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{
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EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
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return Scalar(0);
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}
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};
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template<typename Scalar>
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struct atanh2_impl : atanh2_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {};
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template<typename Scalar>
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struct atanh2_retval
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{
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typedef Scalar type;
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};
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template<typename Scalar>
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inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y)
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{
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return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y);
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}
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/****************************************************************************
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* Implementation of pow *
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****************************************************************************/
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@ -79,7 +79,7 @@ temp = m2 * m3;
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m1 += temp.adjoint(); \endcode</td>
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<td>\code
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m1.noalias() += m3.adjoint()
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* * m2.adjoint(); \endcode</td>
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* * m2.adjoint(); \endcode</td>
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<td>This is because the product expression has the EvalBeforeNesting bit which
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enforces the evaluation of the product by the Tranpose expression.</td>
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</tr>
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@ -51,7 +51,6 @@ private:
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void compute2x2(const MatrixType& A, MatrixType& result);
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void computeBig(const MatrixType& A, MatrixType& result);
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static Scalar atanh2(Scalar y, Scalar x);
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int getPadeDegree(float normTminusI);
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int getPadeDegree(double normTminusI);
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int getPadeDegree(long double normTminusI);
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@ -93,20 +92,6 @@ MatrixType MatrixLogarithmAtomic<MatrixType>::compute(const MatrixType& A)
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return result;
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}
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/** \brief Compute atanh (inverse hyperbolic tangent) for \f$ y / x \f$. */
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template <typename MatrixType>
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typename MatrixType::Scalar MatrixLogarithmAtomic<MatrixType>::atanh2(Scalar y, Scalar x)
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{
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using std::abs;
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using std::sqrt;
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Scalar z = y / x;
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if (abs(z) > sqrt(NumTraits<Scalar>::epsilon()))
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return Scalar(0.5) * log((x + y) / (x - y));
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else
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return z + z*z*z / Scalar(3);
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}
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/** \brief Compute logarithm of 2x2 triangular matrix. */
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template <typename MatrixType>
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void MatrixLogarithmAtomic<MatrixType>::compute2x2(const MatrixType& A, MatrixType& result)
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@ -131,7 +116,7 @@ void MatrixLogarithmAtomic<MatrixType>::compute2x2(const MatrixType& A, MatrixTy
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// computation in previous branch is inaccurate if A(1,1) \approx A(0,0)
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int unwindingNumber = static_cast<int>(ceil((imag(logA11 - logA00) - M_PI) / (2*M_PI)));
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Scalar y = A(1,1) - A(0,0), x = A(1,1) + A(0,0);
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result(0,1) = A(0,1) * (Scalar(2) * atanh2(y,x) + Scalar(0,2*M_PI*unwindingNumber)) / y;
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result(0,1) = A(0,1) * (Scalar(2) * internal::atanh2(y,x) + Scalar(0,2*M_PI*unwindingNumber)) / y;
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}
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}
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@ -111,9 +111,6 @@ class MatrixPower
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*/
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void getFractionalExponent();
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/** \brief Compute atanh (inverse hyperbolic tangent) for \f$ y / x \f$. */
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static ComplexScalar atanh2(const ComplexScalar& y, const ComplexScalar& x);
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/** \brief Compute power of 2x2 triangular matrix. */
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void compute2x2(RealScalar p);
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@ -223,7 +220,7 @@ void MatrixPower<MatrixType,PlainObject>::computeChainProduct(ResultType& result
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int cost = computeCost(p);
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if (m_pInt < RealScalar(0)) {
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if (p * m_dimb <= cost * m_dimA) {
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if (p * m_dimb <= cost * m_dimA && m_dimA > 2) {
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partialPivLuSolve(result, p);
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return;
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} else {
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@ -296,21 +293,6 @@ void MatrixPower<MatrixType,PlainObject>::getFractionalExponent()
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}
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}
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template<typename MatrixType, typename PlainObject>
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std::complex<typename MatrixType::RealScalar>
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MatrixPower<MatrixType,PlainObject>::atanh2(const ComplexScalar& y, const ComplexScalar& x)
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{
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using std::abs;
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using std::log;
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using std::sqrt;
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const ComplexScalar z = y / x;
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if (abs(z) > sqrt(NumTraits<RealScalar>::epsilon()))
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return RealScalar(0.5) * log((x + y) / (x - y));
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else
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return z + z*z*z / RealScalar(3);
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}
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template<typename MatrixType, typename PlainObject>
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void MatrixPower<MatrixType,PlainObject>::compute2x2(RealScalar p)
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{
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@ -337,7 +319,7 @@ void MatrixPower<MatrixType,PlainObject>::compute2x2(RealScalar p)
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} else {
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// computation in previous branch is inaccurate if abs(m_T(j,j)) \approx abs(m_T(i,i))
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unwindingNumber = ceil((imag(m_logTdiag[j] - m_logTdiag[i]) - M_PI) / (2 * M_PI));
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w = atanh2(m_T(j,j) - m_T(i,i), m_T(j,j) + m_T(i,i)) + ComplexScalar(0, M_PI * unwindingNumber);
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w = internal::atanh2(m_T(j,j) - m_T(i,i), m_T(j,j) + m_T(i,i)) + ComplexScalar(0, M_PI * unwindingNumber);
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m_fT(i,j) = m_T(i,j) * RealScalar(2) * exp(RealScalar(0.5) * p * (m_logTdiag[j] + m_logTdiag[i])) *
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sinh(p * w) / (m_T(j,j) - m_T(i,i));
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}
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