diff --git a/Eigen/src/Core/util/Memory.h b/Eigen/src/Core/util/Memory.h index caf1d48ce..0d2c432eb 100644 --- a/Eigen/src/Core/util/Memory.h +++ b/Eigen/src/Core/util/Memory.h @@ -44,7 +44,7 @@ template struct ei_aligned_array T array[Size]; }; -/** \internal allocates \a size * sizeof(\a T) bytes with a 16 bytes based alignment */ +/** \internal allocates \a size * sizeof(\a T) bytes with 16 bytes alignment */ template inline T* ei_aligned_malloc(size_t size) { @@ -91,7 +91,7 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset) /** \internal * ei_alloc_stack(TYPE,SIZE) allocates sizeof(TYPE)*SIZE bytes on the stack if sizeof(TYPE)*SIZE is * smaller than EIGEN_STACK_ALLOCATION_LIMIT. Otherwise the memory is allocated using the operator new. - * Data allocated with ei_alloc_stack \b must be freed calling ei_free_stack(PTR,TYPE,SIZE). + * Data allocated with ei_alloc_stack \b must be freed by calling ei_free_stack(PTR,TYPE,SIZE). * \code * float * data = ei_alloc_stack(float,array.size()); * // ... @@ -108,15 +108,15 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset) /** \class WithAlignedOperatorNew * - * \brief Enforces inherited classes to be 16 bytes aligned when dynamicalled allocated with operator new + * \brief Enforces instances of inherited classes to be 16 bytes aligned when allocated with operator new * * When Eigen's explicit vectorization is enabled, Eigen assumes that some fixed sizes types are aligned - * on a 16 bytes boundary. Such types include: + * on a 16 bytes boundary. Those include all Matrix types having a sizeof multiple of 16 bytes, e.g.: * - Vector2d, Vector4f, Vector4i, Vector4d, * - Matrix2d, Matrix4f, Matrix4i, Matrix4d, * - etc. - * When objects are statically allocated, the compiler will automatically and always enforces 16 bytes - * alignment of the data. However some troubles might appear when data are dynamically allocated. + * When an object is statically allocated, the compiler will automatically and always enforces 16 bytes + * alignment of the data when needed. However some troubles might appear when data are dynamically allocated. * Let's pick an example: * \code * struct Foo { @@ -130,8 +130,8 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset) * pObj2->some_vector = Vector4f(..); // => !! might segfault !! * \endcode * Here, the problem is that operator new is not aware of the compile time alignment requirement of the - * type Vector4f (and hence of the type Foo). Therefore "new Foo" does not necessarily returned a 16 bytes - * aligned pointer. The purpose of the class WithAlignedOperatorNew is exactly to overcome this issue, by + * type Vector4f (and hence of the type Foo). Therefore "new Foo" does not necessarily returns a 16 bytes + * aligned pointer. The purpose of the class WithAlignedOperatorNew is exactly to overcome this issue by * overloading the operator new to return aligned data when the vectorization is enabled. * Here is a similar safe example: * \code @@ -139,12 +139,9 @@ inline static int ei_alignmentOffset(const Scalar* ptr, int maxOffset) * char dummy; * Vector4f some_vector; * }; - * Foo obj1; // static allocation - * obj1.some_vector = Vector4f(..); // => OK - * * Foo *pObj2 = new Foo; // dynamic allocation * pObj2->some_vector = Vector4f(..); // => SAFE ! - * \endcode + * \endcode * * \sa class ei_new_allocator */ @@ -172,7 +169,7 @@ struct WithAlignedOperatorNew void operator delete(void * ptr) { free(ptr); } void operator delete[](void * ptr) { free(ptr); } - + #endif }; @@ -190,14 +187,14 @@ struct ei_with_aligned_operator_new {}; * STL allocator simply wrapping operators new[] and delete[]. Unlike GCC's default new_allocator, * ei_new_allocator call operator new on the type \a T and not the general new operator ignoring * overloaded version of operator new. - * + * * Example: * \code * // Vector4f requires 16 bytes alignment: * std::vector > dataVec4; * // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator: * std::vector dataVec3; - * + * * struct Foo : WithAlignedOperatorNew { * char dummy; * Vector4f some_vector; diff --git a/Eigen/src/Geometry/AlignedBox.h b/Eigen/src/Geometry/AlignedBox.h index fb6d3d0a7..3d4608e73 100644 --- a/Eigen/src/Geometry/AlignedBox.h +++ b/Eigen/src/Geometry/AlignedBox.h @@ -26,6 +26,7 @@ #define EIGEN_ALIGNEDBOX_H /** \geometry_module \ingroup GeometryModule + * \nonstableyet * * \class AlignedBox * diff --git a/Eigen/src/QR/EigenSolver.h b/Eigen/src/QR/EigenSolver.h index 38a383f14..33dcd6daa 100644 --- a/Eigen/src/QR/EigenSolver.h +++ b/Eigen/src/QR/EigenSolver.h @@ -26,6 +26,7 @@ #define EIGEN_EIGENSOLVER_H /** \ingroup QR_Module + * \nonstableyet * * \class EigenSolver * diff --git a/Eigen/src/QR/HessenbergDecomposition.h b/Eigen/src/QR/HessenbergDecomposition.h index 30541670c..21597bb02 100644 --- a/Eigen/src/QR/HessenbergDecomposition.h +++ b/Eigen/src/QR/HessenbergDecomposition.h @@ -26,6 +26,7 @@ #define EIGEN_HESSENBERGDECOMPOSITION_H /** \ingroup QR_Module + * \nonstableyet * * \class HessenbergDecomposition * diff --git a/Eigen/src/QR/QR.h b/Eigen/src/QR/QR.h index e584ee120..c3fe96718 100644 --- a/Eigen/src/QR/QR.h +++ b/Eigen/src/QR/QR.h @@ -26,6 +26,7 @@ #define EIGEN_QR_H /** \ingroup QR_Module + * \nonstableyet * * \class QR * diff --git a/Eigen/src/QR/SelfAdjointEigenSolver.h b/Eigen/src/QR/SelfAdjointEigenSolver.h index fdb2764c4..e57b52ed5 100644 --- a/Eigen/src/QR/SelfAdjointEigenSolver.h +++ b/Eigen/src/QR/SelfAdjointEigenSolver.h @@ -26,6 +26,7 @@ #define EIGEN_SELFADJOINTEIGENSOLVER_H /** \qr_module \ingroup QR_Module + * \nonstableyet * * \class SelfAdjointEigenSolver * @@ -225,7 +226,7 @@ void SelfAdjointEigenSolver:: compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors) { ei_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows()); - + // Compute the cholesky decomposition of matB = L L' LLT cholB(matB); diff --git a/Eigen/src/QR/Tridiagonalization.h b/Eigen/src/QR/Tridiagonalization.h index a9635c961..a4fa32ed4 100644 --- a/Eigen/src/QR/Tridiagonalization.h +++ b/Eigen/src/QR/Tridiagonalization.h @@ -26,6 +26,7 @@ #define EIGEN_TRIDIAGONALIZATION_H /** \ingroup QR_Module + * \nonstableyet * * \class Tridiagonalization * @@ -219,7 +220,7 @@ void Tridiagonalization::_compute(MatrixType& matA, CoeffVectorType& // i.e., A = H' A H where H = I - h v v' and v = matA.col(i).end(n-i-1) matA.col(i).coeffRef(i+1) = 1; - + /* This is the initial algorithm which minimize operation counts and maximize * the use of Eigen's expression. Unfortunately, the first matrix-vector product * using Part is very very slow */ @@ -284,7 +285,7 @@ void Tridiagonalization::_compute(MatrixType& matA, CoeffVectorType& hCoeffs.end(n-i-1) += (h * Scalar(-0.5) * matA.col(i).end(n-i-1).dot(hCoeffs.end(n-i-1))) * matA.col(i).end(n-i-1); - + const Scalar* EIGEN_RESTRICT pb = &matA.coeffRef(0,i); const Scalar* EIGEN_RESTRICT pa = (&hCoeffs.coeffRef(0)) - 1; for (int j1=i+1; j1::_compute(MatrixType& matA, CoeffVectorType& { int alignedStart = (starti) + ei_alignmentOffset(&matA.coeffRef(starti,j1), n-starti); alignedEnd = alignedStart + ((n-alignedStart)/PacketSize)*PacketSize; - + for (int i1=starti; i1 \endcode" \ "addexample=\anchor" \ "label=\bug" \ - "redstar=*" + "redstar=*" \ + "nonstableyet=\warning This class/function is not considered to be part of the stable public API yet. Some (minor) changes might happen in future releases." # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C # sources only. Doxygen will then generate output that is more tailored for C. diff --git a/doc/TutorialGeometry.dox b/doc/TutorialGeometry.dox index 8742341f5..5a1a3e06e 100644 --- a/doc/TutorialGeometry.dox +++ b/doc/TutorialGeometry.dox @@ -67,7 +67,7 @@ might still be interesting to write generic and efficient algorithms taking as i kind of transformations. Any of the above transformation types can be converted to any other types of the same nature, -or to a more generic type. Here are come additional examples: +or to a more generic type. Here are some additional examples: -
\code Rotation2Df r = Matrix2f(..); // assumes a pure rotation matrix @@ -176,7 +176,7 @@ t.pretranslate(Vector_(tx,ty,..)); t *= Translation_(tx,ty,..); t = Translation_(tx,ty,..) * t; \endcode
\b Rotation \n In 2D, any_rotation can also \n be an angle in radian\code +
\b Rotation \n In 2D and for the procedural API, any_rotation can also \n be an angle in radian\code t.rotate(any_rotation); t.prerotate(any_rotation); \endcode\code @@ -216,7 +216,7 @@ t = Translation_(..) * t * RotationType(..) * Translation_(..) * Scaling_(..);
Euler angles might be convenient to create rotation objects. -On the other hand, since there exist 24 differents convensions,they are pretty confusing to use. This example shows how +On the other hand, since there exist 24 differents convension,they are pretty confusing to use. This example shows how to create a rotation matrix according to the 2-1-2 convention.\code Matrix3f m; m = AngleAxisf(angle1, Vector3f::UnitZ())