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Clean up informal language, vague TODOs, and dead code in comments

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libeigen/eigen!2191

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
This commit is contained in:
Rasmus Munk Larsen
2026-02-22 18:32:10 -08:00
parent 7d727d26bc
commit d5e67adbe7
63 changed files with 136 additions and 161 deletions
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6
Eigen/src/Eigenvalues/ComplexSchur.h
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@@ -277,8 +277,8 @@ typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::compu
using std::abs;
if ((iter == 10 || iter == 20) && iu > 1) {
// exceptional shift, taken from http://www.netlib.org/eispack/comqr.f
return ComplexSchur<MatrixType>::ComplexScalar(
abs(numext::real(m_matT.coeff(iu, iu - 1))) + abs(numext::real(m_matT.coeff(iu - 1, iu - 2))));
return ComplexSchur<MatrixType>::ComplexScalar(abs(numext::real(m_matT.coeff(iu, iu - 1))) +
abs(numext::real(m_matT.coeff(iu - 1, iu - 2))));
}
// compute the shift as one of the eigenvalues of t, the 2x2
@@ -363,7 +363,7 @@ struct complex_schur_reduce_to_hessenberg<MatrixType, false> {
_this.m_hess.compute(matrix);
_this.m_matT = _this.m_hess.matrixH().template cast<ComplexScalar>();
if (computeU) {
// This may cause an allocation which seems to be avoidable
// TODO: this temporary allocation could potentially be avoided.
MatrixType Q = _this.m_hess.matrixQ();
_this.m_matU = Q.template cast<ComplexScalar>();
}

2
Eigen/src/Eigenvalues/HessenbergDecomposition.h
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@@ -317,7 +317,7 @@ namespace internal {
* HessenbergDecomposition class until the it is assigned or evaluated for
* some other reason (the reference should remain valid during the life time
* of this object). This class is the return type of
* HessenbergDecomposition::matrixH(); there is probably no other use for this
* HessenbergDecomposition::matrixH(); there is no other intended use for this
* class.
*/
template <typename MatrixType>

3
Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
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@@ -111,8 +111,7 @@ template <typename Derived>
inline typename MatrixBase<Derived>::RealScalar MatrixBase<Derived>::operatorNorm() const {
using std::sqrt;
typename Derived::PlainObject m_eval(derived());
// FIXME if it is really guaranteed that the eigenvalues are already sorted,
// then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
// FIXME: if eigenvalues are guaranteed to be sorted, comparing the first and last is sufficient.
return sqrt((m_eval * m_eval.adjoint()).eval().template selfadjointView<Lower>().eigenvalues().maxCoeff());
}

2
Eigen/src/Eigenvalues/RealSchur.h
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@@ -343,7 +343,7 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMa
template <typename MatrixType>
inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT() {
const Index size = m_matT.cols();
// FIXME to be efficient the following would requires a triangular reduxion code
// FIXME: a triangular reduction would be more efficient here.
// Scalar norm = m_matT.upper().cwiseAbs().sum()
// + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
Scalar norm(0);

9
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@@ -548,8 +548,7 @@ EIGEN_DEVICE_FUNC ComputationInfo computeFromTridiagonal_impl(DiagType& diag, Su
info = NoConvergence;
// Sort eigenvalues and corresponding vectors.
// TODO make the sort optional ?
// TODO use a better sort algorithm !!
// TODO: make the sort optional and use a more efficient sorting algorithm.
if (info == Success) {
for (Index i = 0; i < n - 1; ++i) {
Index k;
@@ -653,12 +652,12 @@ struct direct_selfadjoint_eigenvalues<SolverType, 3, false> {
// Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
Scalar shift = mat.trace() / Scalar(3);
// TODO Avoid this copy. Currently it is necessary to suppress bogus values when determining maxCoeff and for
// computing the eigenvectors later
// TODO: avoid this copy. Currently necessary to suppress bogus values when determining maxCoeff and for
// computing the eigenvectors later.
MatrixType scaledMat = mat.template selfadjointView<Lower>();
scaledMat.diagonal().array() -= shift;
Scalar scale = scaledMat.cwiseAbs().maxCoeff();
if (scale > 0) scaledMat /= scale; // TODO for scale==0 we could save the remaining operations
if (scale > 0) scaledMat /= scale; // TODO: skip remaining operations when scale==0.
// compute the eigenvalues
computeRoots(scaledMat, eivals);