merge with default branch

Eigen/Cholesky

@@ -10,9 +10,11 @@
   *
   *
   * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
-  * Those decompositions are accessible via the following MatrixBase methods:
+  * Those decompositions are also accessible via the following methods:
-  *  - MatrixBase::llt(),
+  *  - MatrixBase::llt()
   *  - MatrixBase::ldlt()
+  *  - SelfAdjointView::llt()
+  *  - SelfAdjointView::ldlt()
   *
   * \code
   * #include <Eigen/Cholesky>

Eigen/src/Cholesky/LDLT.h

@@ -43,7 +43,7 @@ namespace internal {
   * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
   * decomposition to determine whether a system of equations has a solution.
   *
-  * \sa MatrixBase::ldlt(), class LLT
+  * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
   */
 template<typename _MatrixType, int _UpLo> class LDLT
 {
@@ -179,7 +179,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
       * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
       * computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
       *
-      * \sa MatrixBase::ldlt()
+      * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
       */
     template<typename Rhs>
     inline const internal::solve_retval<LDLT, Rhs>
@@ -582,6 +582,7 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
 #ifndef __CUDACC__
 /** \cholesky_module
   * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+  * \sa MatrixBase::ldlt()
   */
 template<typename MatrixType, unsigned int UpLo>
 inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
@@ -592,6 +593,7 @@ SelfAdjointView<MatrixType, UpLo>::ldlt() const
 
 /** \cholesky_module
   * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+  * \sa SelfAdjointView::ldlt()
   */
 template<typename Derived>
 inline const LDLT<typename MatrixBase<Derived>::PlainObject>

Eigen/src/Cholesky/LLT.h

@@ -41,7 +41,7 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
   * Example: \include LLT_example.cpp
   * Output: \verbinclude LLT_example.out
   *    
-  * \sa MatrixBase::llt(), class LDLT
+  * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
   */
  /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
   * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
@@ -115,7 +115,7 @@ template<typename _MatrixType, int _UpLo> class LLT
       * Example: \include LLT_solve.cpp
       * Output: \verbinclude LLT_solve.out
       *
-      * \sa solveInPlace(), MatrixBase::llt()
+      * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
       */
     template<typename Rhs>
     inline const internal::solve_retval<LLT, Rhs>
@@ -468,6 +468,7 @@ MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
 #ifndef __CUDACC__
 /** \cholesky_module
   * \returns the LLT decomposition of \c *this
+  * \sa SelfAdjointView::llt()
   */
 template<typename Derived>
 inline const LLT<typename MatrixBase<Derived>::PlainObject>
@@ -478,6 +479,7 @@ MatrixBase<Derived>::llt() const
 
 /** \cholesky_module
   * \returns the LLT decomposition of \c *this
+  * \sa SelfAdjointView::llt()
   */
 template<typename MatrixType, unsigned int UpLo>
 inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>

Eigen/src/Core/MathFunctions.h

@@ -669,6 +669,15 @@ bool (isfinite)(const T& x)
   return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
 }
 
+template<typename T>
+EIGEN_DEVICE_FUNC
+bool (isfinite)(const std::complex<T>& x)
+{
+  using std::real;
+  using std::imag;
+  return isfinite(real(x)) && isfinite(imag(x));
+}
+
 } // end namespace numext
 
 namespace internal {

Eigen/src/Core/arch/NEON/PacketMath.h

@@ -97,6 +97,7 @@ template<> struct packet_traits<int>    : default_packet_traits
 // workaround gcc 4.2, 4.3 and 4.4 compilatin issue
 EIGEN_STRONG_INLINE float32x4_t vld1q_f32(const float* x) { return ::vld1q_f32((const float32_t*)x); }
 EIGEN_STRONG_INLINE float32x2_t vld1_f32 (const float* x) { return ::vld1_f32 ((const float32_t*)x); }
+EIGEN_STRONG_INLINE float32x2_t vld1_dup_f32 (const float* x) { return ::vld1_dup_f32 ((const float32_t*)x); }
 EIGEN_STRONG_INLINE void        vst1q_f32(float* to, float32x4_t from) { ::vst1q_f32((float32_t*)to,from); }
 EIGEN_STRONG_INLINE void        vst1_f32 (float* to, float32x2_t from) { ::vst1_f32 ((float32_t*)to,from); }
 #endif

Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h

@@ -265,6 +265,8 @@ template<typename MatrixType, unsigned int UpLo>
 template<typename ProductDerived, typename _Lhs, typename _Rhs>
 TriangularView<MatrixType,UpLo>& TriangularView<MatrixType,UpLo>::assignProduct(const ProductBase<ProductDerived, _Lhs,_Rhs>& prod, const Scalar& alpha)
 {
+  eigen_assert(m_matrix.rows() == prod.rows() && m_matrix.cols() == prod.cols());
+
   general_product_to_triangular_selector<MatrixType, ProductDerived, UpLo, (_Lhs::ColsAtCompileTime==1) || (_Rhs::RowsAtCompileTime==1)>::run(m_matrix.const_cast_derived(), prod.derived(), alpha);
   
   return *this;

Eigen/src/Core/util/MKL_support.h

@@ -54,8 +54,25 @@
 #endif
 
 #if defined EIGEN_USE_MKL
+#   include <mkl.h> 
+/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/
+#   ifndef INTEL_MKL_VERSION
+#       undef EIGEN_USE_MKL /* INTEL_MKL_VERSION is not even defined on older versions */
+#   elif INTEL_MKL_VERSION < 100305    /* the intel-mkl-103-release-notes say this was when the lapacke.h interface was added*/
+#       undef EIGEN_USE_MKL
+#   endif
+#   ifndef EIGEN_USE_MKL
+    /*If the MKL version is too old, undef everything*/
+#       undef   EIGEN_USE_MKL_ALL
+#       undef   EIGEN_USE_BLAS
+#       undef   EIGEN_USE_LAPACKE
+#       undef   EIGEN_USE_MKL_VML
+#       undef   EIGEN_USE_LAPACKE_STRICT
+#       undef   EIGEN_USE_LAPACKE
+#   endif
+#endif
 
-#include <mkl.h>
+#if defined EIGEN_USE_MKL
 #include <mkl_lapacke.h>
 #define EIGEN_MKL_VML_THRESHOLD 128
 

Eigen/src/Core/util/Memory.h

@@ -89,7 +89,7 @@ inline void throw_std_bad_alloc()
   #ifdef EIGEN_EXCEPTIONS
     throw std::bad_alloc();
   #else
-    std::size_t huge = -1;
+    std::size_t huge = static_cast<std::size_t>(-1);
     new int[huge];
   #endif
 }

Eigen/src/Eigenvalues/EigenSolver.h

@@ -275,10 +275,11 @@ template<typename _MatrixType> class EigenSolver
       */
     EigenSolver& compute(const MatrixType& matrix, bool computeEigenvectors = true);
 
+    /** \returns NumericalIssue if the input contains INF or NaN values or overflow occured. Returns Success otherwise. */
     ComputationInfo info() const
     {
       eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      return m_realSchur.info();
+      return m_info;
     }
 
     /** \brief Sets the maximum number of iterations allowed. */
@@ -302,6 +303,7 @@ template<typename _MatrixType> class EigenSolver
     EigenvalueType m_eivalues;
     bool m_isInitialized;
     bool m_eigenvectorsOk;
+    ComputationInfo m_info;
     RealSchur<MatrixType> m_realSchur;
     MatrixType m_matT;
 
@@ -366,12 +368,16 @@ EigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvect
 {
   using std::sqrt;
   using std::abs;
+  using std::max;
+  using numext::isfinite;
   eigen_assert(matrix.cols() == matrix.rows());
 
   // Reduce to real Schur form.
   m_realSchur.compute(matrix, computeEigenvectors);
+  
+  m_info = m_realSchur.info();
 
-  if (m_realSchur.info() == Success)
+  if (m_info == Success)
   {
     m_matT = m_realSchur.matrixT();
     if (computeEigenvectors)
@@ -385,14 +391,40 @@ EigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvect
       if (i == matrix.cols() - 1 || m_matT.coeff(i+1, i) == Scalar(0)) 
       {
         m_eivalues.coeffRef(i) = m_matT.coeff(i, i);
+        if(!isfinite(m_eivalues.coeffRef(i)))
+        {
+          m_isInitialized = true;
+          m_eigenvectorsOk = false;
+          m_info = NumericalIssue;
+          return *this;
+        }
         ++i;
       }
       else
       {
         Scalar p = Scalar(0.5) * (m_matT.coeff(i, i) - m_matT.coeff(i+1, i+1));
-        Scalar z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1)));
+        Scalar z;
+        // Compute z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1)));
+        // without overflow
+        {
+          Scalar t0 = m_matT.coeff(i+1, i);
+          Scalar t1 = m_matT.coeff(i, i+1);
+          Scalar maxval = (max)(abs(p),(max)(abs(t0),abs(t1)));
+          t0 /= maxval;
+          t1 /= maxval;
+          Scalar p0 = p/maxval;
+          z = maxval * sqrt(abs(p0 * p0 + t0 * t1));
+        }
+        
         m_eivalues.coeffRef(i)   = ComplexScalar(m_matT.coeff(i+1, i+1) + p, z);
         m_eivalues.coeffRef(i+1) = ComplexScalar(m_matT.coeff(i+1, i+1) + p, -z);
+        if(!(isfinite(m_eivalues.coeffRef(i)) && isfinite(m_eivalues.coeffRef(i+1))))
+        {
+          m_isInitialized = true;
+          m_eigenvectorsOk = false;
+          m_info = NumericalIssue;
+          return *this;
+        }
         i += 2;
       }
     }
@@ -581,7 +613,7 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
     }
     else
     {
-      eigen_assert(0 && "Internal bug in EigenSolver"); // this should not happen
+      eigen_assert(0 && "Internal bug in EigenSolver (INF or NaN has not been detected)"); // this should not happen
     }
   }
 

Eigen/src/SVD/JacobiSVD.h

@@ -415,6 +415,7 @@ void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
                          JacobiRotation<RealScalar> *j_right)
 {
   using std::sqrt;
+  using std::abs;
   Matrix<RealScalar,2,2> m;
   m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
        numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
@@ -428,9 +429,11 @@ void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
   }
   else
   {
-    RealScalar u = d / t;
+    RealScalar t2d2 = numext::hypot(t,d);
-    rot1.c() = RealScalar(1) / sqrt(RealScalar(1) + numext::abs2(u));
+    rot1.c() = abs(t)/t2d2;
-    rot1.s() = rot1.c() * u;
+    rot1.s() = d/t2d2;
+    if(t<RealScalar(0))
+      rot1.s() = -rot1.s();
   }
   m.applyOnTheLeft(0,1,rot1);
   j_right->makeJacobi(m,0,1);

doc/Doxyfile.in

@@ -223,7 +223,7 @@ ALIASES                = "only_for_vectors=This is only for vectors (either row-
                          "note_about_using_kernel_to_study_multiple_solutions=If you need a complete analysis of the space of solutions, take the one solution obtained by this method and add to it elements of the kernel, as determined by kernel()." \
                          "note_about_checking_solutions=This method just tries to find as good a solution as possible. If you want to check whether a solution exists or if it is accurate, just call this function to get a result and then compute the error of this result, or use MatrixBase::isApprox() directly, for instance like this: \code bool a_solution_exists = (A*result).isApprox(b, precision); \endcode This method avoids dividing by zero, so that the non-existence of a solution doesn't by itself mean that you'll get \c inf or \c nan values." \
                          "note_try_to_help_rvo=This function returns the result by value. In order to make that efficient, it is implemented as just a return statement using a special constructor, hopefully allowing the compiler to perform a RVO (return value optimization)." \
-                         "nonstableyet=\warning This is not considered to be part of the stable public API yet. Changes may happen in future releases. See \ref Experimental \"Experimental parts of Eigen\"
+                         "nonstableyet=\warning This is not considered to be part of the stable public API yet. Changes may happen in future releases. See \ref Experimental \"Experimental parts of Eigen\""
                          
 
 ALIASES += "eigenAutoToc=  "
@@ -1583,6 +1583,7 @@ PREDEFINED             = EIGEN_EMPTY_STRUCT \
                          EIGEN_VECTORIZE \
                          EIGEN_QT_SUPPORT \
                          EIGEN_STRONG_INLINE=inline \
+                         EIGEN_DEVICE_FUNC= \
                          "EIGEN2_SUPPORT_STAGE=99" \
                          "EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR)=template<typename OtherDerived> const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const;" \
                          "EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS)=CwiseBinaryOp<internal::scalar_product_op<typename LHS::Scalar, typename RHS::Scalar >, const LHS, const RHS>"

doc/TutorialReductionsVisitorsBroadcasting.dox

@@ -32,7 +32,7 @@ Eigen also provides the \link MatrixBase::norm() norm() \endlink method, which r
 
 These operations can also operate on matrices; in that case, a n-by-p matrix is seen as a vector of size (n*p), so for example the \link MatrixBase::norm() norm() \endlink method returns the "Frobenius" or "Hilbert-Schmidt" norm. We refrain from speaking of the \f$\ell^2\f$ norm of a matrix because that can mean different things.
 
-If you want other \f$\ell^p\f$ norms, use the \link MatrixBase::lpNorm() lpNnorm<p>() \endlink method. The template parameter \a p can take the special value \a Infinity if you want the \f$\ell^\infty\f$ norm, which is the maximum of the absolute values of the coefficients.
+If you want other \f$\ell^p\f$ norms, use the \link MatrixBase::lpNorm() lpNorm<p>() \endlink method. The template parameter \a p can take the special value \a Infinity if you want the \f$\ell^\infty\f$ norm, which is the maximum of the absolute values of the coefficients.
 
 The following example demonstrates these methods.
 

test/CMakeLists.txt

@@ -315,6 +315,9 @@ find_package(CUDA)
 if(CUDA_FOUND)
   
   set(CUDA_PROPAGATE_HOST_FLAGS OFF)
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") 
+    set(CUDA_NVCC_FLAGS "-ccbin /usr/bin/clang" CACHE STRING "nvcc flags" FORCE)
+  endif()
   cuda_include_directories(${CMAKE_CURRENT_BINARY_DIR})
   set(EIGEN_ADD_TEST_FILENAME_EXTENSION  "cu")
   

test/cholesky.cpp

@@ -82,10 +82,6 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
     symm += a1 * a1.adjoint();
   }
 
-  // to test if really Cholesky only uses the upper triangular part, uncomment the following
-  // FIXME: currently that fails !!
-  //symm.template part<StrictlyLower>().setZero();
-
   {
     SquareMatrixType symmUp = symm.template triangularView<Upper>();
     SquareMatrixType symmLo = symm.template triangularView<Lower>();

test/cuda_basic.cu

@@ -129,7 +129,7 @@ void test_cuda_basic()
   CALL_SUBTEST( run_and_compare_to_cuda(prod<Matrix4f,Vector4f>(), nthreads, in, out) );
   
   CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix3f,Vector3f>(), nthreads, in, out) );
-  CALL_SUBTEST( run_and_compare_to_c<uda(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) );
+  CALL_SUBTEST( run_and_compare_to_cuda(diagonal<Matrix4f,Vector4f>(), nthreads, in, out) );
   
   CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues<Matrix3f>(), nthreads, in, out) );
   CALL_SUBTEST( run_and_compare_to_cuda(eigenvalues<Matrix2f>(), nthreads, in, out) );

test/eigensolver_generic.cpp

@@ -121,5 +121,18 @@ void test_eigensolver_generic()
   }
   );
   
+  // regression test for bug 793
+#ifdef EIGEN_TEST_PART_2
+  {
+     MatrixXd a(3,3);
+     a << 0,  0,  1,
+          1,  1, 1,
+          1, 1e+200,  1;
+     Eigen::EigenSolver<MatrixXd> eig(a);
+     VERIFY_IS_APPROX(a * eig.pseudoEigenvectors(), eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix());
+     VERIFY_IS_APPROX(a * eig.eigenvectors(), eig.eigenvectors() * eig.eigenvalues().asDiagonal());
+  }
+#endif
+  
   TEST_SET_BUT_UNUSED_VARIABLE(s)
 }

unsupported/Eigen/CXX11/src/Core/util/CXX11Meta.h

@@ -285,7 +285,7 @@ struct equal_op         { template<typename A, typename B> constexpr static inli
 struct not_equal_op     { template<typename A, typename B> constexpr static inline auto run(A a, B b) -> decltype(a != b)  { return a != b;  } };
 struct lesser_op        { template<typename A, typename B> constexpr static inline auto run(A a, B b) -> decltype(a < b)   { return a < b;   } };
 struct lesser_equal_op  { template<typename A, typename B> constexpr static inline auto run(A a, B b) -> decltype(a <= b)  { return a <= b;  } };
-struct greater_op       { template<typename A, typename B> constexpr static inline auto run(A a, B b) -> decltype(a < b)   { return a < b;   } };
+struct greater_op       { template<typename A, typename B> constexpr static inline auto run(A a, B b) -> decltype(a > b)   { return a > b;   } };
 struct greater_equal_op { template<typename A, typename B> constexpr static inline auto run(A a, B b) -> decltype(a >= b)  { return a >= b;  } };
 
 /* generic unary operations */

unsupported/Eigen/src/CMakeLists.txt

@@ -2,6 +2,7 @@ ADD_SUBDIRECTORY(AutoDiff)
 ADD_SUBDIRECTORY(BVH)
 ADD_SUBDIRECTORY(FFT)
 ADD_SUBDIRECTORY(IterativeSolvers)
+ADD_SUBDIRECTORY(LevenbergMarquardt)
 ADD_SUBDIRECTORY(MatrixFunctions)
 ADD_SUBDIRECTORY(MoreVectorization)
 ADD_SUBDIRECTORY(NonLinearOptimization)

unsupported/Eigen/src/LevenbergMarquardt/CMakeLists.txt

@@ -2,5 +2,5 @@ FILE(GLOB Eigen_LevenbergMarquardt_SRCS "*.h")
 
 INSTALL(FILES
   ${Eigen_LevenbergMarquardt_SRCS}
-  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LevenbergMarquardt COMPONENT Devel
+  DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen/src/LevenbergMarquardt COMPONENT Devel
   )