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MIsc. source and comment typos

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Found using `codespell` and `grep` from downstream FreeCAD
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luz.paz
2018-03-11 10:01:44 -04:00
parent 624df50945
commit e3912f5e63
98 changed files with 122 additions and 122 deletions
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2
Eigen/src/Cholesky/LDLT.h
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@@ -247,7 +247,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the factorization failed because of a zero pivot.
*/
ComputationInfo info() const

2
Eigen/src/Cholesky/LLT.h
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@@ -180,7 +180,7 @@ template<typename _MatrixType, int _UpLo> class LLT
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix.appears not to be positive definite.
*/
ComputationInfo info() const

4
Eigen/src/Core/AssignEvaluator.h
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@@ -756,7 +756,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType
// AssignmentKind must define a Kind typedef.
template<typename DstShape, typename SrcShape> struct AssignmentKind;
// Assignement kind defined in this file:
// Assignment kind defined in this file:
struct Dense2Dense {};
struct EigenBase2EigenBase {};
@@ -899,7 +899,7 @@ struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
src.evalTo(dst);
}
// NOTE The following two functions are templated to avoid their instanciation if not needed
// NOTE The following two functions are templated to avoid their instantiation if not needed
// This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC

2
Eigen/src/Core/DenseBase.h
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@@ -395,7 +395,7 @@ template<typename Derived> class DenseBase
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*
* \warning Be carefull with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
* \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE EvalReturnType eval() const

2
Eigen/src/Core/DenseStorage.h
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@@ -61,7 +61,7 @@ struct plain_array
#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#elif EIGEN_GNUC_AT_LEAST(4,7)
// GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned.
// GCC 4.7 is too aggressive in its optimizations and remove the alignment test based on the fact the array is declared to be aligned.
// See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
// Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
template<typename PtrType>

2
Eigen/src/Core/MathFunctions.h
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@@ -749,7 +749,7 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
}
// Implementatin of is* functions
// Implementation of is* functions
// std::is* do not work with fast-math and gcc, std::is* are available on MSVC 2013 and newer, as well as in clang.
#if (EIGEN_HAS_CXX11_MATH && !(EIGEN_COMP_GNUC_STRICT && __FINITE_MATH_ONLY__)) || (EIGEN_COMP_MSVC>=1800) || (EIGEN_COMP_CLANG)

4
Eigen/src/Core/NoAlias.h
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@@ -75,10 +75,10 @@ class NoAlias
*
* More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag.
* Currently, even though several expressions may alias, only product
* expressions have this flag. Therefore, noalias() is only usefull when
* expressions have this flag. Therefore, noalias() is only useful when
* the source expression contains a matrix product.
*
* Here are some examples where noalias is usefull:
* Here are some examples where noalias is useful:
* \code
* D.noalias() = A * B;
* D.noalias() += A.transpose() * B;

2
Eigen/src/Core/PlainObjectBase.h
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@@ -780,7 +780,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
resize(size);
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitely converted)
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted)
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>::type* = 0)

2
Eigen/src/Core/Product.h
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@@ -116,7 +116,7 @@ class dense_product_base
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{};
/** Convertion to scalar for inner-products */
/** Conversion to scalar for inner-products */
template<typename Lhs, typename Rhs, int Option>
class dense_product_base<Lhs, Rhs, Option, InnerProduct>
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type

2
Eigen/src/Core/Transpositions.h
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@@ -84,7 +84,7 @@ class TranspositionsBase
}
// FIXME: do we want such methods ?
// might be usefull when the target matrix expression is complex, e.g.:
// might be useful when the target matrix expression is complex, e.g.:
// object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..);
/*
template<typename MatrixType>

4
Eigen/src/Core/TriangularMatrix.h
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@@ -470,7 +470,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
* \a Side==OnTheLeft (the default), or the right-inverse-multiply \a other * inverse(\c *this) if
* \a Side==OnTheRight.
*
* Note that the template parameter \c Side can be ommitted, in which case \c Side==OnTheLeft
* Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
*
* The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
* diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
@@ -496,7 +496,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* Note that the template parameter \c Side can be ommitted, in which case \c Side==OnTheLeft
* Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
*
* See TriangularView:solve() for the details.
*/

4
Eigen/src/Core/arch/AltiVec/PacketMath.h
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@@ -434,7 +434,7 @@ template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
return (Packet4i) vec_perm(MSQ, LSQ, mask); // align the data
}
#else
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
// We also need to redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
{
EIGEN_DEBUG_UNALIGNED_LOAD
@@ -500,7 +500,7 @@ template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& f
vec_st( MSQ, 0, (unsigned char *)to ); // Store the MSQ part
}
#else
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
// We also need to redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from)
{
EIGEN_DEBUG_ALIGNED_STORE

2
Eigen/src/Core/arch/SSE/MathFunctions.h
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@@ -242,7 +242,7 @@ Packet2d pexp<Packet2d>(const Packet2d& _x)
return pmax(pmul(x, Packet2d(_mm_castsi128_pd(emm0))), _x);
}
/* evaluation of 4 sines at onces, using SSE2 intrinsics.
/* evaluation of 4 sines at once, using SSE2 intrinsics.
The code is the exact rewriting of the cephes sinf function.
Precision is excellent as long as x < 8192 (I did not bother to

4
Eigen/src/Core/products/GeneralBlockPanelKernel.h
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@@ -1523,7 +1523,7 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
prefetch(&blA[0]);
const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
// The following piece of code wont work for 512 bit registers
// The following piece of code won't work for 512 bit registers
// Moreover, if LhsProgress==8 it assumes that there is a half packet of the same size
// as nr (which is currently 4) for the return type.
typedef typename unpacket_traits<SResPacket>::half SResPacketHalf;
@@ -1924,7 +1924,7 @@ EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Co
// const Scalar* b6 = &rhs[(j2+6)*rhsStride];
// const Scalar* b7 = &rhs[(j2+7)*rhsStride];
// Index k=0;
// if(PacketSize==8) // TODO enbale vectorized transposition for PacketSize==4
// if(PacketSize==8) // TODO enable vectorized transposition for PacketSize==4
// {
// for(; k<peeled_k; k+=PacketSize) {
// PacketBlock<Packet> kernel;

2
Eigen/src/Core/products/GeneralMatrixVector.h
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@@ -201,7 +201,7 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,C
}
/* Optimized row-major matrix * vector product:
* This algorithm processes 4 rows at onces that allows to both reduce
* This algorithm processes 4 rows at once that allows to both reduce
* the number of load/stores of the result by a factor 4 and to reduce
* the instruction dependency. Moreover, we know that all bands have the
* same alignment pattern.

2
Eigen/src/Core/products/Parallelizer.h
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@@ -117,7 +117,7 @@ void parallelize_gemm(const Functor& func, Index rows, Index cols, Index depth,
// compute the number of threads we are going to use
Index threads = std::min<Index>(nbThreads(), pb_max_threads);
// if multi-threading is explicitely disabled, not useful, or if we already are in a parallel session,
// if multi-threading is explicitly disabled, not useful, or if we already are in a parallel session,
// then abort multi-threading
// FIXME omp_get_num_threads()>1 only works for openmp, what if the user does not use openmp?
if((!Condition) || (threads==1) || (omp_get_num_threads()>1))

2
Eigen/src/Core/products/SelfadjointMatrixVector.h
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@@ -15,7 +15,7 @@ namespace Eigen {
namespace internal {
/* Optimized selfadjoint matrix * vector product:
* This algorithm processes 2 columns at onces that allows to both reduce
* This algorithm processes 2 columns at once that allows to both reduce
* the number of load/stores of the result by a factor 2 and to reduce
* the instruction dependency.
*/

4
Eigen/src/Core/util/Macros.h
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@@ -719,7 +719,7 @@ namespace Eigen {
#error EIGEN_MAX_STATIC_ALIGN_BYTES and EIGEN_DONT_ALIGN[_STATICALLY] are both defined with EIGEN_MAX_STATIC_ALIGN_BYTES!=0. Use EIGEN_MAX_STATIC_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN_STATICALLY.
#endif
// EIGEN_DONT_ALIGN_STATICALLY and EIGEN_DONT_ALIGN are deprectated
// EIGEN_DONT_ALIGN_STATICALLY and EIGEN_DONT_ALIGN are deprecated
// They imply EIGEN_MAX_STATIC_ALIGN_BYTES=0
#if defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN)
#ifdef EIGEN_MAX_STATIC_ALIGN_BYTES
@@ -778,7 +778,7 @@ namespace Eigen {
#endif
// At this stage, EIGEN_MAX_STATIC_ALIGN_BYTES>0 is the true test whether we want to align arrays on the stack or not.
// It takes into account both the user choice to explicitly enable/disable alignment (by settting EIGEN_MAX_STATIC_ALIGN_BYTES)
// It takes into account both the user choice to explicitly enable/disable alignment (by setting EIGEN_MAX_STATIC_ALIGN_BYTES)
// and the architecture config (EIGEN_ARCH_WANTS_STACK_ALIGNMENT).
// Henceforth, only EIGEN_MAX_STATIC_ALIGN_BYTES should be used.

2
Eigen/src/Core/util/Memory.h
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@@ -703,7 +703,7 @@ template<typename T> void swap(scoped_array<T> &a,scoped_array<T> &b)
* - 32 bytes alignment if AVX is enabled.
* - 64 bytes alignment if AVX512 is enabled.
*
* This can be controled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
* This can be controlled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
* \link TopicPreprocessorDirectivesPerformance there \endlink.
*
* Example:

2
Eigen/src/Core/util/Meta.h
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@@ -272,7 +272,7 @@ template<> struct numeric_limits<unsigned long long>
#endif
/** \internal
* A base class do disable default copy ctor and copy assignement operator.
* A base class do disable default copy ctor and copy assignment operator.
*/
class noncopyable
{

2
Eigen/src/Eigenvalues/ComplexEigenSolver.h
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@@ -214,7 +214,7 @@ template<typename _MatrixType> class ComplexEigenSolver
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful, \c NoConvergence otherwise.
* \returns \c Success if computation was successful, \c NoConvergence otherwise.
*/
ComputationInfo info() const
{

2
Eigen/src/Eigenvalues/ComplexSchur.h
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@@ -212,7 +212,7 @@ template<typename _MatrixType> class ComplexSchur
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful, \c NoConvergence otherwise.
* \returns \c Success if computation was successful, \c NoConvergence otherwise.
*/
ComputationInfo info() const
{

2
Eigen/src/Eigenvalues/EigenSolver.h
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@@ -277,7 +277,7 @@ template<typename _MatrixType> class EigenSolver
template<typename InputType>
EigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
/** \returns NumericalIssue if the input contains INF or NaN values or overflow occured. Returns Success otherwise. */
/** \returns NumericalIssue if the input contains INF or NaN values or overflow occurred. Returns Success otherwise. */
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "EigenSolver is not initialized.");

2
Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
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@@ -121,7 +121,7 @@ class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixT
*
* \returns Reference to \c *this
*
* Accoring to \p options, this function computes eigenvalues and (if requested)
* According to \p options, this function computes eigenvalues and (if requested)
* the eigenvectors of one of the following three generalized eigenproblems:
* - \c Ax_lBx: \f$ Ax = \lambda B x \f$
* - \c ABx_lx: \f$ ABx = \lambda x \f$

2
Eigen/src/Eigenvalues/RealQZ.h
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@@ -161,7 +161,7 @@ namespace Eigen {
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful, \c NoConvergence otherwise.
* \returns \c Success if computation was successful, \c NoConvergence otherwise.
*/
ComputationInfo info() const
{

2
Eigen/src/Eigenvalues/RealSchur.h
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@@ -190,7 +190,7 @@ template<typename _MatrixType> class RealSchur
RealSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU);
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful, \c NoConvergence otherwise.
* \returns \c Success if computation was successful, \c NoConvergence otherwise.
*/
ComputationInfo info() const
{

2
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@@ -337,7 +337,7 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful, \c NoConvergence otherwise.
* \returns \c Success if computation was successful, \c NoConvergence otherwise.
*/
EIGEN_DEVICE_FUNC
ComputationInfo info() const

2
Eigen/src/Geometry/Scaling.h
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@@ -128,7 +128,7 @@ public:
/** Concatenates a linear transformation matrix and a uniform scaling
* \relates UniformScaling
*/
// NOTE this operator is defiend in MatrixBase and not as a friend function
// NOTE this operator is defined in MatrixBase and not as a friend function
// of UniformScaling to fix an internal crash of Intel's ICC
template<typename Derived,typename Scalar>
EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,Scalar,product)

4
Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
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@@ -136,7 +136,7 @@ class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageInd
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const
@@ -230,7 +230,7 @@ void IncompleteLUT<Scalar,StorageIndex>::analyzePattern(const _MatrixType& amat)
SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
SparseMatrix<Scalar,ColMajor, StorageIndex> mat2 = amat.transpose();
// FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
// on the other hand for a really non-symmetric pattern, mat2*mat1 should be prefered...
// on the other hand for a really non-symmetric pattern, mat2*mat1 should be preferred...
SparseMatrix<Scalar,ColMajor, StorageIndex> AtA = mat2 + mat1;
AMDOrdering<StorageIndex> ordering;
ordering(AtA,m_P);

2
Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
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@@ -275,7 +275,7 @@ public:
const Preconditioner& preconditioner() const { return m_preconditioner; }
/** \returns the max number of iterations.
* It is either the value setted by setMaxIterations or, by default,
* It is either the value set by setMaxIterations or, by default,
* twice the number of columns of the matrix.
*/
Index maxIterations() const

2
Eigen/src/KLUSupport/KLUSupport.h
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@@ -106,7 +106,7 @@ class KLU : public SparseSolverBase<KLU<_MatrixType> >
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const

2
Eigen/src/LU/FullPivLU.h
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@@ -48,7 +48,7 @@ template<typename _MatrixType> struct traits<FullPivLU<_MatrixType> >
* The data of the LU decomposition can be directly accessed through the methods matrixLU(),
* permutationP(), permutationQ().
*
* As an exemple, here is how the original matrix can be retrieved:
* As an example, here is how the original matrix can be retrieved:
* \include class_FullPivLU.cpp
* Output: \verbinclude class_FullPivLU.out
*

4
Eigen/src/LU/PartialPivLU.h
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@@ -420,8 +420,8 @@ struct partial_lu_impl
* \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
*
* \note This very low level interface using pointers, etc. is to:
* 1 - reduce the number of instanciations to the strict minimum
* 2 - avoid infinite recursion of the instanciations with Block<Block<Block<...> > >
* 1 - reduce the number of instantiations to the strict minimum
* 2 - avoid infinite recursion of the instantiations with Block<Block<Block<...> > >
*/
static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions, PivIndex& nb_transpositions, Index maxBlockSize=256)
{

6
Eigen/src/OrderingMethods/Eigen_Colamd.h
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@@ -1493,7 +1493,7 @@ static inline void order_children
c = Col [c].shared1.parent ;
/* continue until we hit an ordered column. There are */
/* guarranteed not to be anymore unordered columns */
/* guaranteed not to be anymore unordered columns */
/* above an ordered column */
} while (Col [c].shared2.order == COLAMD_EMPTY) ;
@@ -1638,7 +1638,7 @@ static void detect_super_cols
COLAMD_ASSERT (ROW_IS_ALIVE (*cp1)) ;
COLAMD_ASSERT (ROW_IS_ALIVE (*cp2)) ;
/* row indices will same order for both supercols, */
/* no gather scatter nessasary */
/* no gather scatter necessary */
if (*cp1++ != *cp2++)
{
break ;
@@ -1688,7 +1688,7 @@ static void detect_super_cols
/*
Defragments and compacts columns and rows in the workspace A. Used when
all avaliable memory has been used while performing row merging. Returns
all available memory has been used while performing row merging. Returns
the index of the first free position in A, after garbage collection. The
time taken by this routine is linear is the size of the array A, which is
itself linear in the number of nonzeros in the input matrix.

2
Eigen/src/PaStiXSupport/PaStiXSupport.h
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@@ -203,7 +203,7 @@ class PastixBase : public SparseSolverBase<Derived>
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the PaStiX reports a problem
* \c InvalidInput if the input matrix is invalid
*

2
Eigen/src/PardisoSupport/PardisoSupport.h
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@@ -140,7 +140,7 @@ class PardisoImpl : public SparseSolverBase<Derived>
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix appears to be negative.
*/
ComputationInfo info() const

2
Eigen/src/QR/ColPivHouseholderQR.h
View File

@@ -402,7 +402,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
*/
RealScalar maxPivot() const { return m_maxpivot; }
/** \brief Reports whether the QR factorization was succesful.
/** \brief Reports whether the QR factorization was successful.
*
* \note This function always returns \c Success. It is provided for compatibility
* with other factorization routines.

2
Eigen/src/QR/CompleteOrthogonalDecomposition.h
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@@ -353,7 +353,7 @@ class CompleteOrthogonalDecomposition {
inline RealScalar maxPivot() const { return m_cpqr.maxPivot(); }
/** \brief Reports whether the complete orthogonal decomposition was
* succesful.
* successful.
*
* \note This function always returns \c Success. It is provided for
* compatibility

2
Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
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@@ -220,7 +220,7 @@ class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the sparse QR can not be computed
*/
ComputationInfo info() const

2
Eigen/src/SVD/BDCSVD.h
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@@ -62,7 +62,7 @@ struct traits<BDCSVD<_MatrixType> >
* recommended and can several order of magnitude faster.
*
* \warning this algorithm is unlikely to provide accurate result when compiled with unsafe math optimizations.
* For instance, this concerns Intel's compiler (ICC), which perfroms such optimization by default unless
* For instance, this concerns Intel's compiler (ICC), which performs such optimization by default unless
* you compile with the \c -fp-model \c precise option. Likewise, the \c -ffast-math option of GCC or clang will
* significantly degrade the accuracy.
*

4
Eigen/src/SVD/UpperBidiagonalization.h
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@@ -202,7 +202,7 @@ void upperbidiagonalization_blocked_helper(MatrixType& A,
{
SubColumnType y_k( Y.col(k).tail(remainingCols) );
// let's use the begining of column k of Y as a temporary vector
// let's use the beginning of column k of Y as a temporary vector
SubColumnType tmp( Y.col(k).head(k) );
y_k.noalias() = A.block(k,k+1, remainingRows,remainingCols).adjoint() * v_k; // bottleneck
tmp.noalias() = V_k1.adjoint() * v_k;
@@ -231,7 +231,7 @@ void upperbidiagonalization_blocked_helper(MatrixType& A,
{
SubColumnType x_k ( X.col(k).tail(remainingRows-1) );
// let's use the begining of column k of X as a temporary vectors
// let's use the beginning of column k of X as a temporary vectors
// note that tmp0 and tmp1 overlaps
SubColumnType tmp0 ( X.col(k).head(k) ),
tmp1 ( X.col(k).head(k+1) );

2
Eigen/src/SparseCholesky/SimplicialCholesky.h
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@@ -101,7 +101,7 @@ class SimplicialCholeskyBase : public SparseSolverBase<Derived>
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const

6
Eigen/src/SparseCore/SparseMatrix.h
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@@ -21,7 +21,7 @@ namespace Eigen {
* This class implements a more versatile variants of the common \em compressed row/column storage format.
* Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index.
* All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra
* space inbetween the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
* space in between the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
* can be done with limited memory reallocation and copies.
*
* A call to the function makeCompressed() turns the matrix into the standard \em compressed format
@@ -503,7 +503,7 @@ class SparseMatrix
}
}
/** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerence \a epsilon */
/** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerance \a epsilon */
void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
{
prune(default_prunning_func(reference,epsilon));
@@ -986,7 +986,7 @@ void set_from_triplets(const InputIterator& begin, const InputIterator& end, Spa
*
* \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
* an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
* be explicitely stored into a std::vector for instance.
* be explicitly stored into a std::vector for instance.
*/
template<typename Scalar, int _Options, typename _StorageIndex>
template<typename InputIterators>

2
Eigen/src/SparseCore/SparseProduct.h
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@@ -17,7 +17,7 @@ namespace Eigen {
* The automatic pruning of the small values can be achieved by calling the pruned() function
* in which case a totally different product algorithm is employed:
* \code
* C = (A*B).pruned(); // supress numerical zeros (exact)
* C = (A*B).pruned(); // suppress numerical zeros (exact)
* C = (A*B).pruned(ref);
* C = (A*B).pruned(ref,epsilon);
* \endcode

2
Eigen/src/SparseCore/SparseVector.h
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@@ -281,7 +281,7 @@ class SparseVector
}
/** Swaps the values of \c *this and \a other.
* Overloaded for performance: this version performs a \em shallow swap by swaping pointers and attributes only.
* Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
* \sa SparseMatrixBase::swap()
*/
inline void swap(SparseVector& other)

2
Eigen/src/SparseLU/SparseLU.h
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@@ -193,7 +193,7 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
* \c InvalidInput if the input matrix is invalid
*

2
Eigen/src/SparseLU/SparseLU_Memory.h
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@@ -51,7 +51,7 @@ inline Index LUTempSpace(Index&m, Index& w)
/**
* Expand the existing storage to accomodate more fill-ins
* Expand the existing storage to accommodate more fill-ins
* \param vec Valid pointer to the vector to allocate or expand
* \param[in,out] length At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
* \param[in] nbElts Current number of elements in the factors

4
Eigen/src/SparseLU/SparseLU_column_dfs.h
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@@ -151,7 +151,7 @@ Index SparseLUImpl<Scalar,StorageIndex>::column_dfs(const Index m, const Index j
StorageIndex ito = glu.xlsub(fsupc+1);
glu.xlsub(jcolm1) = ito;
StorageIndex istop = ito + jptr - jm1ptr;
xprune(jcolm1) = istop; // intialize xprune(jcol-1)
xprune(jcolm1) = istop; // initialize xprune(jcol-1)
glu.xlsub(jcol) = istop;
for (StorageIndex ifrom = jm1ptr; ifrom < nextl; ++ifrom, ++ito)
@@ -166,7 +166,7 @@ Index SparseLUImpl<Scalar,StorageIndex>::column_dfs(const Index m, const Index j
// Tidy up the pointers before exit
glu.xsup(nsuper+1) = jcolp1;
glu.supno(jcolp1) = nsuper;
xprune(jcol) = StorageIndex(nextl); // Intialize upper bound for pruning
xprune(jcol) = StorageIndex(nextl); // Initialize upper bound for pruning
glu.xlsub(jcolp1) = StorageIndex(nextl);
return 0;

2
Eigen/src/SparseLU/SparseLU_gemm_kernel.h
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@@ -215,7 +215,7 @@ void sparselu_gemm(Index m, Index n, Index d, const Scalar* A, Index lda, const
if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize); }\
pstore(C0+i+(I)*PacketSize, c0);
// agressive vectorization and peeling
// aggressive vectorization and peeling
for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
{
EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL2");

2
Eigen/src/SparseLU/SparseLU_panel_bmod.h
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@@ -38,7 +38,7 @@ namespace internal {
* \brief Performs numeric block updates (sup-panel) in topological order.
*
* Before entering this routine, the original nonzeros in the panel
* were already copied i nto the spa[m,w]
* were already copied into the spa[m,w]
*
* \param m number of rows in the matrix
* \param w Panel size

2
Eigen/src/SuperLUSupport/SuperLUSupport.h
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@@ -352,7 +352,7 @@ class SuperLUBase : public SparseSolverBase<Derived>
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const

2
Eigen/src/UmfPackSupport/UmfPackSupport.h
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@@ -201,7 +201,7 @@ class UmfPackLU : public SparseSolverBase<UmfPackLU<_MatrixType> >
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const

2
Eigen/src/plugins/IndexedViewMethods.h
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@@ -112,7 +112,7 @@ operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_IND
#if EIGEN_HAS_STATIC_ARRAY_TEMPLATE
// The folowing three overloads are needed to handle raw Index[N] arrays.
// The following three overloads are needed to handle raw Index[N] arrays.
template<typename RowIndicesT, std::size_t RowIndicesN, typename ColIndices>
IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],typename IvcColType<ColIndices>::type>