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Fix comment typos, doubled words, grammar errors, and copy-paste mistakes

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libeigen/eigen!2173

Closes #3034

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
This commit is contained in:
Rasmus Munk Larsen
2026-02-21 14:36:21 -08:00
parent 0e424f4050
commit e6accc73ff
28 changed files with 48 additions and 48 deletions
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2
Eigen/src/Core/Array.h
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@@ -141,7 +141,7 @@ class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxR
* This constructor is for 1D array or vectors with more than 4 coefficients.
*
* \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
* constructor must match the the fixed number of rows (resp. columns) of \c *this.
* constructor must match the fixed number of rows (resp. columns) of \c *this.
*
*
* Example: \include Array_variadic_ctor_cxx11.cpp

4
Eigen/src/Core/ArrayWrapper.h
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@@ -21,7 +21,7 @@ namespace Eigen {
* \brief Expression of a mathematical vector or matrix as an array object
*
* This class is the return type of MatrixBase::array(), and most of the time
* this is the only way it is use.
* this is the only way it is used.
*
* \sa MatrixBase::array(), class MatrixWrapper
*/
@@ -96,7 +96,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > {
* \brief Expression of an array as a mathematical vector or matrix
*
* This class is the return type of ArrayBase::matrix(), and most of the time
* this is the only way it is use.
* this is the only way it is used.
*
* \sa MatrixBase::matrix(), class ArrayWrapper
*/

2
Eigen/src/Core/DiagonalMatrix.h
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@@ -61,7 +61,7 @@ class DiagonalBase : public EigenBase<Derived> {
/**
* Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
* not an expression.
* \returns A dense matrix, with its diagonal entries set from the the derived object. */
* \returns A dense matrix, with its diagonal entries set from the derived object. */
EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const { return derived(); }
/** \returns a reference to the derived object's vector of diagonal coefficients. */

4
Eigen/src/Core/Dot.h
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@@ -54,7 +54,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
* In both cases, it consists in the sum of the square of all the matrix entries.
* For vectors, this is also equals to the dot product of \c *this with itself.
* For vectors, this is also equal to the dot product of \c *this with itself.
*
* \sa dot(), norm(), lpNorm()
*/
@@ -66,7 +66,7 @@ MatrixBase<Derived>::squaredNorm() const {
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the square root of the sum of the square of all the matrix entries.
* For vectors, this is also equals to the square root of the dot product of \c *this with itself.
* For vectors, this is also equal to the square root of the dot product of \c *this with itself.
*
* \sa lpNorm(), dot(), squaredNorm()
*/

6
Eigen/src/Core/GenericPacketMath.h
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@@ -610,7 +610,7 @@ EIGEN_DEVICE_FUNC inline bool pselect<bool>(const bool& cond, const bool& a, con
return cond ? a : b;
}
/** \internal \returns the min or of \a a and \a b (coeff-wise)
/** \internal \returns the min or max of \a a and \a b (coeff-wise)
If either \a a or \a b are NaN, the result is implementation defined. */
template <int NaNPropagation, bool IsInteger>
struct pminmax_impl {
@@ -648,7 +648,7 @@ struct pminmax_impl<PropagateNumbers, false> {
#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) [](const Type& aa, const Type& bb) { return Func(aa, bb); }
/** \internal \returns the min of \a a and \a b (coeff-wise).
If \a a or \b b is NaN, the return value is implementation defined. */
If \a a or \a b is NaN, the return value is implementation defined. */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
return numext::mini(a, b);
@@ -663,7 +663,7 @@ EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
}
/** \internal \returns the max of \a a and \a b (coeff-wise)
If \a a or \b b is NaN, the return value is implementation defined. */
If \a a or \a b is NaN, the return value is implementation defined. */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) {
return numext::maxi(a, b);

2
Eigen/src/Core/Matrix.h
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@@ -271,7 +271,7 @@ class Matrix : public PlainObjectBase<Matrix<Scalar_, Rows_, Cols_, Options_, Ma
* This constructor is for 1D array or vectors with more than 4 coefficients.
*
* \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
* constructor must match the the fixed number of rows (resp. columns) of \c *this.
* constructor must match the fixed number of rows (resp. columns) of \c *this.
*
*
* Example: \include Matrix_variadic_ctor_cxx11.cpp

2
Eigen/src/Core/PlainObjectBase.h
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@@ -461,7 +461,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type {
* This constructor is for 1D array or vectors with more than 4 coefficients.
*
* \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
* constructor must match the the fixed number of rows (resp. columns) of \c *this.
* constructor must match the fixed number of rows (resp. columns) of \c *this.
*/
template <typename... ArgTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2,

2
Eigen/src/Core/SkewSymmetricMatrix3.h
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@@ -62,7 +62,7 @@ class SkewSymmetricBase : public EigenBase<Derived> {
/**
* Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
* not an expression.
* \returns A dense matrix, with its entries set from the the derived object. */
* \returns A dense matrix, with its entries set from the derived object. */
EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const { return derived(); }
/** Determinant vanishes */

4
Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
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@@ -1753,7 +1753,7 @@ EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psqrt_complex(const P
const RealPacket cst_imag_sign_mask = pset1<Packet>(Scalar(RealScalar(0.0), RealScalar(-0.0))).v;
RealPacket imag_signs = pand(a.v, cst_imag_sign_mask);
Packet negative_real_result;
// Notice that rho is positive, so taking it's absolute value is a noop.
// Notice that rho is positive, so taking its absolute value is a noop.
negative_real_result.v = por(pabs(pcplxflip(positive_real_result).v), imag_signs);
// Step 5. Select solution branch based on the sign of the real parts.
@@ -1877,7 +1877,7 @@ struct psign_impl<Packet, std::enable_if_t<!is_scalar<Packet>::value &&
}
};
// \internal \returns the the sign of a complex number z, defined as z / abs(z).
// \internal \returns the sign of a complex number z, defined as z / abs(z).
template <typename Packet>
struct psign_impl<Packet, std::enable_if_t<!is_scalar<Packet>::value &&
NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&

2
Eigen/src/Core/util/Macros.h
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@@ -595,7 +595,7 @@
// without an explicit launch_bounds attribute is called with a threads_per_block value
// greater than 256.
//
// This is a regression in functioanlity and is expected to be fixed within the next
// This is a regression in functionality and is expected to be fixed within the next
// couple of ROCm releases (compiler will go back to using 1024 value as the default)
//
// In the meantime, we will use a "only enabled for HIP" macro to set the launch_bounds

4
Eigen/src/Eigenvalues/ComplexQZ.h
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@@ -203,7 +203,7 @@ class ComplexQZ {
inline Mat2 computeZk2(const Row2& b);
// This is basically taken from from Eigen3::RealQZ
// This is basically taken from Eigen3::RealQZ
void hessenbergTriangular(const MatrixType& A, const MatrixType& B);
// This function can be called when m_Q and m_Z are initialized and m_S, m_T
@@ -243,7 +243,7 @@ void ComplexQZ<MatrixType_>::compute(const MatrixType& A, const MatrixType& B, b
reduceHessenbergTriangular();
}
// This is basically taken from from Eigen3::RealQZ
// This is basically taken from Eigen3::RealQZ
template <typename MatrixType_>
void ComplexQZ<MatrixType_>::hessenbergTriangular(const MatrixType& A, const MatrixType& B) {
// Copy A and B, these will be the matrices on which we operate later

6
Eigen/src/Geometry/OrthoMethods.h
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@@ -200,7 +200,7 @@ struct unitOrthogonal_selector<Derived, 3> {
EIGEN_DEVICE_FUNC static inline VectorType run(const Derived& src) {
VectorType perp;
/* Let us compute the crossed product of *this with a vector
* that is not too close to being colinear to *this.
* that is not too close to being collinear to *this.
*/
/* unless the x and y coords are both close to zero, we can
@@ -213,7 +213,7 @@ struct unitOrthogonal_selector<Derived, 3> {
perp.coeffRef(2) = 0;
}
/* if both x and y are close to zero, then the vector is close
* to the z-axis, so it's far from colinear to the x-axis for instance.
* to the z-axis, so it's far from collinear to the x-axis for instance.
* So we take the crossed product with (1,0,0) and normalize it.
*/
else {
@@ -242,7 +242,7 @@ struct unitOrthogonal_selector<Derived, 2> {
* \returns a unit vector which is orthogonal to \c *this
*
* The size of \c *this must be at least 2. If the size is exactly 2,
* then the returned vector is a counter clock wise rotation of \c *this, i.e., (-y,x).normalized().
* then the returned vector is a counter-clockwise rotation of \c *this, i.e., (-y,x).normalized().
*
* \sa cross()
*/

4
Eigen/src/Geometry/Rotation2D.h
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@@ -23,7 +23,7 @@ namespace Eigen {
*
* \tparam Scalar_ the scalar type, i.e., the type of the coefficients
*
* This class is equivalent to a single scalar representing a counter clock wise rotation
* This class is equivalent to a single scalar representing a counter-clockwise rotation
* as a single angle in radian. It provides some additional features such as the automatic
* conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar
* interface to Quaternion in order to facilitate the writing of generic algorithms
@@ -57,7 +57,7 @@ class Rotation2D : public RotationBase<Rotation2D<Scalar_>, 2> {
Scalar m_angle;
public:
/** Construct a 2D counter clock wise rotation from the angle \a a in radian. */
/** Construct a 2D counter-clockwise rotation from the angle \a a in radian. */
EIGEN_DEVICE_FUNC explicit inline Rotation2D(const Scalar& a) : m_angle(a) {}
/** Default constructor without initialization. The represented rotation is undefined. */

2
Eigen/src/Geometry/Transform.h
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@@ -745,7 +745,7 @@ Transform<Scalar, Dim, Mode, Options>& Transform<Scalar, Dim, Mode, Options>::op
/** \returns a QMatrix from \c *this assuming the dimension is 2.
*
* \warning this conversion might loss data if \c *this is not affine
* \warning this conversion might lose data if \c *this is not affine
*
* This function is available only if the token EIGEN_QT_SUPPORT is defined.
*/

2
Eigen/src/Jacobi/Jacobi.h
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@@ -239,7 +239,7 @@ EIGEN_DEVICE_FUNC void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const
namespace internal {
/** \jacobi_module
* Applies the clock wise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
* Applies the clockwise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
* \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right ) = J \left ( \begin{array}{cc} x \\ y \end{array} \right )
* \f$
*

6
Eigen/src/KLUSupport/KLUSupport.h
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@@ -112,7 +112,7 @@ class KLU : public SparseSolverBase<KLU<MatrixType_> > {
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
return m_info;
}
/** Computes the sparse Cholesky decomposition of \a matrix
/** Computes the sparse LU factorization of \a matrix
* Note that the matrix should be column-major, and in compressed format for best performance.
* \sa SparseMatrix::makeCompressed().
*/
@@ -147,7 +147,7 @@ class KLU : public SparseSolverBase<KLU<MatrixType_> > {
*/
inline const klu_common &kluCommon() const { return m_common; }
/** Provides access to the control settings array used by UmfPack.
/** Provides access to the control settings array used by KLU.
*
* If this array contains NaN's, the default values are used.
*
@@ -157,7 +157,7 @@ class KLU : public SparseSolverBase<KLU<MatrixType_> > {
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the pattern anylysis has been performed.
* The given matrix must have the same sparsity as the matrix on which the pattern analysis has been performed.
*
* \sa analyzePattern(), compute()
*/

2
Eigen/src/MetisSupport/MetisSupport.h
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@@ -38,7 +38,7 @@ class MetisOrdering {
IndexVector visited(m);
visited.setConstant(-1);
for (StorageIndex j = 0; j < m; j++) {
// Compute the union structure of of A(j,:) and At(j,:)
// Compute the union structure of A(j,:) and At(j,:)
visited(j) = j; // Do not include the diagonal element
// Get the nonzeros in row/column j of A
for (typename MatrixType::InnerIterator it(A, j); it; ++it) {

2
Eigen/src/OrderingMethods/Eigen_Colamd.h
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@@ -713,7 +713,7 @@ static void init_scoring(
for (c = n_col - 1; c >= 0; c--) {
deg = Col[c].length;
if (deg == 0) {
/* this is a empty column, kill and order it last */
/* this is an empty column, kill and order it last */
Col[c].shared2.order = --n_col2;
Col[c].kill_principal();
}

2
Eigen/src/PardisoSupport/PardisoSupport.h
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@@ -157,7 +157,7 @@ class PardisoImpl : public SparseSolverBase<Derived> {
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()

2
Eigen/src/SVD/BDCSVD.h
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@@ -453,7 +453,7 @@ void BDCSVD<MatrixType, Options>::copyUV(const HouseholderU& householderU, const
* A = [A1]
* [A2]
* such that A1.rows()==n1, then we assume that at least half of the columns of A1 and A2 are zeros.
* We can thus pack them prior to the the matrix product. However, this is only worth the effort if the matrix is large
* We can thus pack them prior to the matrix product. However, this is only worth the effort if the matrix is large
* enough.
*/
template <typename MatrixType, int Options>

4
Eigen/src/SVD/UpperBidiagonalization.h
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@@ -330,7 +330,7 @@ UpperBidiagonalization<MatrixType_>& UpperBidiagonalization<MatrixType_>::comput
Index cols = matrix.cols();
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
eigen_assert(rows >= cols && "UpperBidiagonalization is only for matrices satisfying rows>=cols.");
m_householder = matrix;
@@ -350,7 +350,7 @@ UpperBidiagonalization<MatrixType_>& UpperBidiagonalization<MatrixType_>::comput
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
eigen_assert(rows >= cols && "UpperBidiagonalization is only for matrices satisfying rows>=cols.");
m_householder = matrix;
upperbidiagonalization_inplace_blocked(m_householder, m_bidiagonal);

10
Eigen/src/SparseCholesky/SimplicialCholesky.h
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@@ -416,7 +416,7 @@ class SimplicialLLT : public SimplicialCholeskyBase<SimplicialLLT<MatrixType_, U
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()
@@ -505,7 +505,7 @@ class SimplicialLDLT : public SimplicialCholeskyBase<SimplicialLDLT<MatrixType_,
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()
@@ -587,7 +587,7 @@ class SimplicialNonHermitianLLT
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()
@@ -677,7 +677,7 @@ class SimplicialNonHermitianLDLT
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()
@@ -761,7 +761,7 @@ class SimplicialCholesky : public SimplicialCholeskyBase<SimplicialCholesky<Matr
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()

4
Eigen/src/SparseLU/SparseLU.h
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@@ -360,7 +360,7 @@ class SparseLU : public SparseSolverBase<SparseLU<MatrixType_, OrderingType_>>,
/** \brief Give the absolute value of the determinant.
*
* \returns the absolute value of the determinant of the matrix of which
* *this is the QR decomposition.
* *this is the LU factorization.
*
* \warning a determinant can be very big or small, so for matrices
* of large enough dimension, there is a risk of overflow/underflow.
@@ -389,7 +389,7 @@ class SparseLU : public SparseSolverBase<SparseLU<MatrixType_, OrderingType_>>,
/** \brief Give the natural log of the absolute determinant.
*
* \returns the natural log of the absolute value of the determinant of the matrix
* of which **this is the QR decomposition
* of which *this is the LU factorization
*
* \note This method is useful to work around the risk of overflow/underflow that's
* inherent to the determinant computation.

4
Eigen/src/SuperLUSupport/SuperLUSupport.h
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@@ -487,7 +487,7 @@ class SuperLU : public SuperLUBase<MatrixType_, SuperLU<MatrixType_> > {
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()
@@ -792,7 +792,7 @@ class SuperILU : public SuperLUBase<MatrixType_, SuperILU<MatrixType_> > {
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the symbolic decomposition has been
* The given matrix must have the same sparsity as the matrix on which the symbolic decomposition has been
* performed.
*
* \sa analyzePattern()

2
Eigen/src/ThreadPool/ThreadLocal.h
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@@ -90,7 +90,7 @@ struct ThreadLocalNoOpRelease {
//
// Eigen::ThreadLocal<Counter> counter(10);
//
// // Each thread will have access to it's own counter object.
// // Each thread will have access to its own counter object.
// Counter& cnt = counter.local();
// cnt++;
//

2
Eigen/src/UmfPackSupport/UmfPackSupport.h
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@@ -425,7 +425,7 @@ class UmfPackLU : public SparseSolverBase<UmfPackLU<MatrixType_> > {
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must have the same sparsity than the matrix on which the pattern anylysis has been performed.
* The given matrix must have the same sparsity as the matrix on which the pattern analysis has been performed.
*
* \sa analyzePattern(), compute()
*/

4
Eigen/src/plugins/BlockMethods.inc
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@@ -990,7 +990,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ConstNColsBlockXpr<N>::Type middl
/// Example: \include MatrixBase_block_int_int.cpp
/// Output: \verbinclude MatrixBase_block_int_int.out
///
/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
/// \note The usage of this overload is discouraged from %Eigen 3.4, better use the generic
/// block(Index,Index,NRowsType,NColsType), here is the one-to-one equivalence:
/// \code
/// mat.template block<NRows,NCols>(i,j) <--> mat.block(i,j,fix<NRows>,fix<NCols>)
@@ -1032,7 +1032,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename ConstFixedBlockXpr<NRows, N
/// Example: \include MatrixBase_template_int_int_block_int_int_int_int.cpp
/// Output: \verbinclude MatrixBase_template_int_int_block_int_int_int_int.out
///
/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
/// \note The usage of this overload is discouraged from %Eigen 3.4, better use the generic
/// block(Index,Index,NRowsType,NColsType), here is the one-to-one complete equivalence:
/// \code
/// mat.template block<NRows,NCols>(i,j,rows,cols) <--> mat.block(i,j,fix<NRows>(rows),fix<NCols>(cols))