Skip to content

clarify eigen docstring #1364

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
May 28, 2025
Merged

clarify eigen docstring #1364

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion src/eigen.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -182,7 +182,7 @@ end
eigen(A; permute::Bool=true, scale::Bool=true, sortby) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an [`Eigen`](@ref) factorization object `F`
which contains the eigenvalues in `F.values` and the eigenvectors in the columns of the
which contains the eigenvalues in `F.values` and the normalized eigenvectors in the columns of the
matrix `F.vectors`. This corresponds to solving an eigenvalue problem of the form
`Ax = λx`, where `A` is a matrix, `x` is an eigenvector, and `λ` is an eigenvalue.
(The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)
Expand Down
6 changes: 3 additions & 3 deletions src/symmetriceigen.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ end
eigen(A::Union{Hermitian, Symmetric}; alg::LinearAlgebra.Algorithm = LinearAlgebra.default_eigen_alg(A)) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an [`Eigen`](@ref) factorization object `F`
which contains the eigenvalues in `F.values` and the eigenvectors in the columns of the
which contains the eigenvalues in `F.values` and the orthonormal eigenvectors in the columns of the
matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.
Expand Down Expand Up @@ -76,7 +76,7 @@ eigen!(A::RealHermSymComplexHerm{<:BlasReal,<:StridedMatrix}, irange::UnitRange)
eigen(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an [`Eigen`](@ref) factorization object `F`
which contains the eigenvalues in `F.values` and the eigenvectors in the columns of the
which contains the eigenvalues in `F.values` and the orthonormal eigenvectors in the columns of the
matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.
Expand All @@ -101,7 +101,7 @@ eigen!(A::RealHermSymComplexHerm{T,<:StridedMatrix}, vl::Real, vh::Real) where {
eigen(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an [`Eigen`](@ref) factorization object `F`
which contains the eigenvalues in `F.values` and the eigenvectors in the columns of the
which contains the eigenvalues in `F.values` and the orthonormal eigenvectors in the columns of the
matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.
Expand Down