Fix bug #314: - remove most of the metaprogramming kung fu in MathFunctions.h (only keep functions that differs from the std) - remove the overloads for array expression that were in the std namespace
diff --git a/unsupported/Eigen/src/FFT/ei_kissfft_impl.h b/unsupported/Eigen/src/FFT/ei_kissfft_impl.h index 37f5af4..be51b4e 100644 --- a/unsupported/Eigen/src/FFT/ei_kissfft_impl.h +++ b/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
@@ -28,6 +28,7 @@ inline void make_twiddles(int nfft,bool inverse) { + using std::acos; m_inverse = inverse; m_twiddles.resize(nfft); Scalar phinc = (inverse?2:-2)* acos( (Scalar) -1) / nfft; @@ -399,6 +400,7 @@ inline Complex * real_twiddles(int ncfft2) { + using std::acos; std::vector<Complex> & twidref = m_realTwiddles[ncfft2];// creates new if not there if ( (int)twidref.size() != ncfft2 ) { twidref.resize(ncfft2);
diff --git a/unsupported/Eigen/src/IterativeSolvers/IncompleteCholesky.h b/unsupported/Eigen/src/IterativeSolvers/IncompleteCholesky.h index 5bc41c0..746d294 100644 --- a/unsupported/Eigen/src/IterativeSolvers/IncompleteCholesky.h +++ b/unsupported/Eigen/src/IterativeSolvers/IncompleteCholesky.h
@@ -116,6 +116,7 @@ template<typename _MatrixType> void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType& mat) { + using std::sqrt; eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); // FIXME Stability: We should probably compute the scaling factors and the shifts that are needed to ensure a succesful LLT factorization and an efficient preconditioner. @@ -182,7 +183,7 @@ m_info = NumericalIssue; return; } - RealScalar rdiag = internal::sqrt(RealScalar(diag)); + RealScalar rdiag = sqrt(RealScalar(diag)); Scalar scal = Scalar(1)/rdiag; vals[colPtr[j]] = rdiag; // Insert the largest p elements in the matrix and scale them meanwhile
diff --git a/unsupported/Eigen/src/IterativeSolvers/IterationController.h b/unsupported/Eigen/src/IterativeSolvers/IterationController.h index aaf46d5..ea86dfe 100644 --- a/unsupported/Eigen/src/IterativeSolvers/IterationController.h +++ b/unsupported/Eigen/src/IterativeSolvers/IterationController.h
@@ -129,7 +129,8 @@ bool converged() const { return m_res <= m_rhsn * m_resmax; } bool converged(double nr) { - m_res = internal::abs(nr); + using std::abs; + m_res = abs(nr); m_resminreach = (std::min)(m_resminreach, m_res); return converged(); }
diff --git a/unsupported/Eigen/src/IterativeSolvers/MINRES.h b/unsupported/Eigen/src/IterativeSolvers/MINRES.h index 46d7bed..6bc1b8f 100644 --- a/unsupported/Eigen/src/IterativeSolvers/MINRES.h +++ b/unsupported/Eigen/src/IterativeSolvers/MINRES.h
@@ -32,6 +32,7 @@ const Preconditioner& precond, int& iters, typename Dest::RealScalar& tol_error) { + using std::sqrt; typedef typename Dest::RealScalar RealScalar; typedef typename Dest::Scalar Scalar; typedef Matrix<Scalar,Dynamic,1> VectorType;
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h index e87a28f..7d42664 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
@@ -235,6 +235,7 @@ template <typename MatrixType, typename AtomicType> void MatrixFunction<MatrixType,AtomicType,1>::partitionEigenvalues() { + using std::abs; const Index rows = m_T.rows(); VectorType diag = m_T.diagonal(); // contains eigenvalues of A @@ -251,14 +252,14 @@ // Look for other element to add to the set for (Index j=i+1; j<rows; ++j) { - if (internal::abs(diag(j) - diag(i)) <= separation() && std::find(qi->begin(), qi->end(), diag(j)) == qi->end()) { - typename ListOfClusters::iterator qj = findCluster(diag(j)); - if (qj == m_clusters.end()) { - qi->push_back(diag(j)); - } else { - qi->insert(qi->end(), qj->begin(), qj->end()); - m_clusters.erase(qj); - } + if (abs(diag(j) - diag(i)) <= separation() && std::find(qi->begin(), qi->end(), diag(j)) == qi->end()) { + typename ListOfClusters::iterator qj = findCluster(diag(j)); + if (qj == m_clusters.end()) { + qi->push_back(diag(j)); + } else { + qi->insert(qi->end(), qj->begin(), qj->end()); + m_clusters.erase(qj); + } } } }
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h index e1e5b77..6ec870d 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
@@ -125,6 +125,7 @@ template <typename MatrixType> void MatrixLogarithmAtomic<MatrixType>::computeBig(const MatrixType& A, MatrixType& result) { + using std::pow; int numberOfSquareRoots = 0; int numberOfExtraSquareRoots = 0; int degree; @@ -141,7 +142,7 @@ degree = getPadeDegree(normTminusI); int degree2 = getPadeDegree(normTminusI / RealScalar(2)); if ((degree - degree2 <= 1) || (numberOfExtraSquareRoots == 1)) - break; + break; ++numberOfExtraSquareRoots; } MatrixType sqrtT;
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h index 3786510..abbf640 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
@@ -99,11 +99,12 @@ void MatrixSquareRootQuasiTriangular<MatrixType>::computeDiagonalPartOfSqrt(MatrixType& sqrtT, const MatrixType& T) { + using std::sqrt; const Index size = m_A.rows(); for (Index i = 0; i < size; i++) { if (i == size - 1 || T.coeff(i+1, i) == 0) { eigen_assert(T(i,i) > 0); - sqrtT.coeffRef(i,i) = internal::sqrt(T.coeff(i,i)); + sqrtT.coeffRef(i,i) = sqrt(T.coeff(i,i)); } else { compute2x2diagonalBlock(sqrtT, T, i); @@ -289,6 +290,7 @@ template <typename ResultType> void MatrixSquareRootTriangular<MatrixType>::compute(ResultType &result) { + using std::sqrt; // Compute Schur decomposition of m_A const ComplexSchur<MatrixType> schurOfA(m_A); const MatrixType& T = schurOfA.matrixT(); @@ -299,7 +301,7 @@ result.resize(m_A.rows(), m_A.cols()); typedef typename MatrixType::Index Index; for (Index i = 0; i < m_A.rows(); i++) { - result.coeffRef(i,i) = internal::sqrt(T.coeff(i,i)); + result.coeffRef(i,i) = sqrt(T.coeff(i,i)); } for (Index j = 1; j < m_A.cols(); j++) { for (Index i = j-1; i >= 0; i--) {
diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h index d9ce4ea..b190827 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h +++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -52,7 +52,7 @@ Parameters() : factor(Scalar(100.)) , maxfev(1000) - , xtol(internal::sqrt(NumTraits<Scalar>::epsilon())) + , xtol(std::sqrt(NumTraits<Scalar>::epsilon())) , nb_of_subdiagonals(-1) , nb_of_superdiagonals(-1) , epsfcn(Scalar(0.)) {} @@ -70,7 +70,7 @@ HybridNonLinearSolverSpace::Status hybrj1( FVectorType &x, - const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon()) + const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) ); HybridNonLinearSolverSpace::Status solveInit(FVectorType &x); @@ -79,7 +79,7 @@ HybridNonLinearSolverSpace::Status hybrd1( FVectorType &x, - const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon()) + const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) ); HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType &x); @@ -185,6 +185,8 @@ HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x) { + using std::abs; + assert(x.size()==n); // check the caller is not cheating us Index j; @@ -276,7 +278,7 @@ ++ncsuc; if (ratio >= Scalar(.5) || ncsuc > 1) delta = (std::max)(delta, pnorm / Scalar(.5)); - if (internal::abs(ratio - 1.) <= Scalar(.1)) { + if (abs(ratio - 1.) <= Scalar(.1)) { delta = pnorm / Scalar(.5); } } @@ -423,6 +425,9 @@ HybridNonLinearSolverSpace::Status HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType &x) { + using std::sqrt; + using std::abs; + assert(x.size()==n); // check the caller is not cheating us Index j; @@ -516,7 +521,7 @@ ++ncsuc; if (ratio >= Scalar(.5) || ncsuc > 1) delta = (std::max)(delta, pnorm / Scalar(.5)); - if (internal::abs(ratio - 1.) <= Scalar(.1)) { + if (abs(ratio - 1.) <= Scalar(.1)) { delta = pnorm / Scalar(.5); } }
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h index 075faee..4b1a2d0 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h +++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -55,8 +55,8 @@ Parameters() : factor(Scalar(100.)) , maxfev(400) - , ftol(internal::sqrt(NumTraits<Scalar>::epsilon())) - , xtol(internal::sqrt(NumTraits<Scalar>::epsilon())) + , ftol(std::sqrt(NumTraits<Scalar>::epsilon())) + , xtol(std::sqrt(NumTraits<Scalar>::epsilon())) , gtol(Scalar(0.)) , epsfcn(Scalar(0.)) {} Scalar factor; @@ -72,7 +72,7 @@ LevenbergMarquardtSpace::Status lmder1( FVectorType &x, - const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon()) + const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) ); LevenbergMarquardtSpace::Status minimize(FVectorType &x); @@ -83,12 +83,12 @@ FunctorType &functor, FVectorType &x, Index *nfev, - const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon()) + const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) ); LevenbergMarquardtSpace::Status lmstr1( FVectorType &x, - const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon()) + const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) ); LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType &x); @@ -206,6 +206,9 @@ LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x) { + using std::abs; + using std::sqrt; + assert(x.size()==n); // check the caller is not cheating us /* calculate the jacobian matrix. */ @@ -249,7 +252,7 @@ if (fnorm != 0.) for (Index j = 0; j < n; ++j) if (wa2[permutation.indices()[j]] != 0.) - gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); + gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); /* test for convergence of the gradient norm. */ if (gnorm <= parameters.gtol) @@ -288,7 +291,7 @@ /* the scaled directional derivative. */ wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1); temp1 = internal::abs2(wa3.stableNorm() / fnorm); - temp2 = internal::abs2(internal::sqrt(par) * pnorm / fnorm); + temp2 = internal::abs2(sqrt(par) * pnorm / fnorm); prered = temp1 + temp2 / Scalar(.5); dirder = -(temp1 + temp2); @@ -326,9 +329,9 @@ } /* tests for convergence. */ - if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm) + if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm) return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall; - if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.) + if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.) return LevenbergMarquardtSpace::RelativeReductionTooSmall; if (delta <= parameters.xtol * xnorm) return LevenbergMarquardtSpace::RelativeErrorTooSmall; @@ -336,7 +339,7 @@ /* tests for termination and stringent tolerances. */ if (nfev >= parameters.maxfev) return LevenbergMarquardtSpace::TooManyFunctionEvaluation; - if (internal::abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.) + if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.) return LevenbergMarquardtSpace::FtolTooSmall; if (delta <= NumTraits<Scalar>::epsilon() * xnorm) return LevenbergMarquardtSpace::XtolTooSmall; @@ -423,6 +426,9 @@ LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorType &x) { + using std::abs; + using std::sqrt; + assert(x.size()==n); // check the caller is not cheating us Index i, j; @@ -496,7 +502,7 @@ if (fnorm != 0.) for (j = 0; j < n; ++j) if (wa2[permutation.indices()[j]] != 0.) - gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); + gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); /* test for convergence of the gradient norm. */ if (gnorm <= parameters.gtol) @@ -535,7 +541,7 @@ /* the scaled directional derivative. */ wa3 = fjac.topLeftCorner(n,n).template triangularView<Upper>() * (permutation.inverse() * wa1); temp1 = internal::abs2(wa3.stableNorm() / fnorm); - temp2 = internal::abs2(internal::sqrt(par) * pnorm / fnorm); + temp2 = internal::abs2(sqrt(par) * pnorm / fnorm); prered = temp1 + temp2 / Scalar(.5); dirder = -(temp1 + temp2); @@ -573,9 +579,9 @@ } /* tests for convergence. */ - if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm) + if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm) return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall; - if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.) + if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.) return LevenbergMarquardtSpace::RelativeReductionTooSmall; if (delta <= parameters.xtol * xnorm) return LevenbergMarquardtSpace::RelativeErrorTooSmall; @@ -583,7 +589,7 @@ /* tests for termination and stringent tolerances. */ if (nfev >= parameters.maxfev) return LevenbergMarquardtSpace::TooManyFunctionEvaluation; - if (internal::abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.) + if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.) return LevenbergMarquardtSpace::FtolTooSmall; if (delta <= NumTraits<Scalar>::epsilon() * xnorm) return LevenbergMarquardtSpace::XtolTooSmall;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/chkder.h b/unsupported/Eigen/src/NonLinearOptimization/chkder.h index ad37c50..db8ff7d 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/chkder.h +++ b/unsupported/Eigen/src/NonLinearOptimization/chkder.h
@@ -16,6 +16,10 @@ Matrix< Scalar, Dynamic, 1 > &err ) { + using std::sqrt; + using std::abs; + using std::log; + typedef DenseIndex Index; const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());
diff --git a/unsupported/Eigen/src/NonLinearOptimization/covar.h b/unsupported/Eigen/src/NonLinearOptimization/covar.h index c73a096..c2fb794 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/covar.h +++ b/unsupported/Eigen/src/NonLinearOptimization/covar.h
@@ -6,8 +6,9 @@ void covar( Matrix< Scalar, Dynamic, Dynamic > &r, const VectorXi &ipvt, - Scalar tol = sqrt(NumTraits<Scalar>::epsilon()) ) + Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) ) { + using std::abs; typedef DenseIndex Index; /* Local variables */
diff --git a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h index 4fbc98b..57dbc8b 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h +++ b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -10,6 +10,9 @@ Scalar delta, Matrix< Scalar, Dynamic, 1 > &x) { + using std::abs; + using std::sqrt; + typedef DenseIndex Index; /* Local variables */
diff --git a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h index 1cabe69..0594793 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h +++ b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
@@ -11,6 +11,9 @@ DenseIndex ml, DenseIndex mu, Scalar epsfcn) { + using std::sqrt; + using std::abs; + typedef DenseIndex Index; /* Local variables */
diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h index 8aac575..834407c 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h +++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -12,6 +12,8 @@ Scalar &par, Matrix< Scalar, Dynamic, 1 > &x) { + using std::abs; + using std::sqrt; typedef DenseIndex Index; /* Local variables */ @@ -168,6 +170,8 @@ Matrix< Scalar, Dynamic, 1 > &x) { + using std::sqrt; + using std::abs; typedef DenseIndex Index; /* Local variables */
diff --git a/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h index d848cb4..7ee30e1 100644 --- a/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h +++ b/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
@@ -63,11 +63,13 @@ */ int df(const InputType& _x, JacobianType &jac) const { + using std::sqrt; + using std::abs; /* Local variables */ Scalar h; int nfev=0; const typename InputType::Index n = _x.size(); - const Scalar eps = internal::sqrt(((std::max)(epsfcn,NumTraits<Scalar>::epsilon() ))); + const Scalar eps = sqrt(((std::max)(epsfcn,NumTraits<Scalar>::epsilon() ))); ValueType val1, val2; InputType x = _x; // TODO : we should do this only if the size is not already known @@ -89,7 +91,7 @@ // Function Body for (int j = 0; j < n; ++j) { - h = eps * internal::abs(x[j]); + h = eps * abs(x[j]); if (h == 0.) { h = eps; }
diff --git a/unsupported/Eigen/src/Polynomials/Companion.h b/unsupported/Eigen/src/Polynomials/Companion.h index 4badd9d..b515c29 100644 --- a/unsupported/Eigen/src/Polynomials/Companion.h +++ b/unsupported/Eigen/src/Polynomials/Companion.h
@@ -210,6 +210,7 @@ template< typename _Scalar, int _Deg > void companion<_Scalar,_Deg>::balance() { + using std::abs; EIGEN_STATIC_ASSERT( Deg == Dynamic || 1 < Deg, YOU_MADE_A_PROGRAMMING_MISTAKE ); const Index deg = m_monic.size(); const Index deg_1 = deg-1;
diff --git a/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/unsupported/Eigen/src/Polynomials/PolynomialSolver.h index 70b873d..fba8fc9 100644 --- a/unsupported/Eigen/src/Polynomials/PolynomialSolver.h +++ b/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
@@ -69,10 +69,11 @@ inline void realRoots( Stl_back_insertion_sequence& bi_seq, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const { + using std::abs; bi_seq.clear(); for(Index i=0; i<m_roots.size(); ++i ) { - if( internal::abs( m_roots[i].imag() ) < absImaginaryThreshold ){ + if( abs( m_roots[i].imag() ) < absImaginaryThreshold ){ bi_seq.push_back( m_roots[i].real() ); } } } @@ -118,13 +119,14 @@ bool& hasArealRoot, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const { + using std::abs; hasArealRoot = false; Index res=0; RealScalar abs2(0); for( Index i=0; i<m_roots.size(); ++i ) { - if( internal::abs( m_roots[i].imag() ) < absImaginaryThreshold ) + if( abs( m_roots[i].imag() ) < absImaginaryThreshold ) { if( !hasArealRoot ) { @@ -144,7 +146,7 @@ } else { - if( internal::abs( m_roots[i].imag() ) < internal::abs( m_roots[res].imag() ) ){ + if( abs( m_roots[i].imag() ) < abs( m_roots[res].imag() ) ){ res = i; } } } @@ -158,13 +160,14 @@ bool& hasArealRoot, const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const { + using std::abs; hasArealRoot = false; Index res=0; RealScalar val(0); for( Index i=0; i<m_roots.size(); ++i ) { - if( internal::abs( m_roots[i].imag() ) < absImaginaryThreshold ) + if( abs( m_roots[i].imag() ) < absImaginaryThreshold ) { if( !hasArealRoot ) { @@ -184,7 +187,7 @@ } else { - if( internal::abs( m_roots[i].imag() ) < internal::abs( m_roots[res].imag() ) ){ + if( abs( m_roots[i].imag() ) < abs( m_roots[res].imag() ) ){ res = i; } } }
diff --git a/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/unsupported/Eigen/src/Polynomials/PolynomialUtils.h index c23204c..5a9ab11 100644 --- a/unsupported/Eigen/src/Polynomials/PolynomialUtils.h +++ b/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
@@ -74,6 +74,7 @@ inline typename NumTraits<typename Polynomial::Scalar>::Real cauchy_max_bound( const Polynomial& poly ) { + using std::abs; typedef typename Polynomial::Scalar Scalar; typedef typename NumTraits<Scalar>::Real Real; @@ -82,7 +83,7 @@ Real cb(0); for( DenseIndex i=0; i<poly.size()-1; ++i ){ - cb += internal::abs(poly[i]*inv_leading_coeff); } + cb += abs(poly[i]*inv_leading_coeff); } return cb + Real(1); } @@ -96,6 +97,7 @@ inline typename NumTraits<typename Polynomial::Scalar>::Real cauchy_min_bound( const Polynomial& poly ) { + using std::abs; typedef typename Polynomial::Scalar Scalar; typedef typename NumTraits<Scalar>::Real Real; @@ -107,7 +109,7 @@ const Scalar inv_min_coeff = Scalar(1)/poly[i]; Real cb(1); for( DenseIndex j=i+1; j<poly.size(); ++j ){ - cb += internal::abs(poly[j]*inv_min_coeff); } + cb += abs(poly[j]*inv_min_coeff); } return Real(1)/cb; }