Deprecate eig to eigen and destructuring via iteration.

NEWS.md

@@ -713,6 +713,21 @@ Deprecated or removed
  `lu!`, `schur!`, `lq!`, `qr!`, `ldlt!`, `svd!`, `bunchkaufman!`,
  `hessenberg!`, and `eigen!` ([#27159]).
 
+ * `eig(A[, args...])` has been deprecated in favor of `eigen(A[, args...])`.
+ Whereas the former returns a tuple of arrays, the latter returns an `Eigen` object.
+ So for a direct replacement, use `(eigen(A[, args...])...,)`. But going forward,
+ consider using the direct result of `eigen(A[, args...])` instead, either
+ destructured into its components (`vals, vecs = eigen(A[, args...])`) or
+ as an `Eigen` object (`X = eigen(A[, args...])`) ([#26997], [#27159], [#27212]).
+
+ * `eig(A::AbstractMatrix, B::AbstractMatrix)` and `eig(A::Number, B::Number)`
+ have been deprecated in favor of `eigen(A, B)`. Whereas the former each return
+ a tuple of arrays, the latter returns a `GeneralizedEigen` object. So for a direct
+ replacement, use `(eigen(A, B)...,)`. But going forward, consider using the
+ direct result of `eigen(A, B)` instead, either destructured into its components
+ (`vals, vecs = eigen(A, B)`), or as a `GeneralizedEigen` object
+ (`X = eigen(A, B)`) ([#26997], [#27159], [#27212]).
+
  * Indexing into multidimensional arrays with more than one index but fewer indices than there are
  dimensions is no longer permitted when those trailing dimensions have lengths greater than 1.
  Instead, reshape the array or add trailing indices so the dimensionality and number of indices

stdlib/LinearAlgebra/docs/src/index.md

@@ -198,7 +198,7 @@ Legend:
 
 ### Matrix factorizations
 
-| Matrix type | LAPACK | [`eig`](@ref) | [`eigvals`](@ref) | [`eigvecs`](@ref) | [`svd`](@ref) | [`svdvals`](@ref) |
+| Matrix type | LAPACK | [`eigen`](@ref) | [`eigvals`](@ref) | [`eigvecs`](@ref) | [`svd`](@ref) | [`svdvals`](@ref) |
 |:------------------------- |:------ |:------------- |:----------------- |:----------------- |:------------- |:----------------- |
 | [`Symmetric`](@ref) | SY | | ARI | | | |
 | [`Hermitian`](@ref) | HE | | ARI | | | |
@@ -331,7 +331,6 @@ LinearAlgebra.lq!
 LinearAlgebra.lq
 LinearAlgebra.bunchkaufman
 LinearAlgebra.bunchkaufman!
-LinearAlgebra.eig
 LinearAlgebra.eigvals
 LinearAlgebra.eigvals!
 LinearAlgebra.eigmax

stdlib/LinearAlgebra/src/LinearAlgebra.jl

@@ -80,7 +80,6 @@ export
  diagind,
  diagm,
  dot,
- eig,
  eigen,
  eigen!,
  eigmax,

stdlib/LinearAlgebra/src/deprecated.jl

@@ -1410,3 +1410,44 @@ export eigfact!
 @deprecate(cholfact!(A::RealHermSymComplexHerm{<:BlasReal,<:StridedMatrix}, ::Val{true}; tol = 0.0), cholesky!(A, Val(true); tol=tol))
 @deprecate(cholfact!(A::RealHermSymComplexHerm{<:Real}, ::Val{true}; tol = 0.0), cholesky!(A, Val(true); tol=tol))
 @deprecate(cholfact!(A::StridedMatrix, ::Val{true}; tol = 0.0), cholesky!(A, Val(true); tol=tol))
+
+# deprecate eig in favor of eigen and destructuring via iteration
+# deprecate eig(...) in favor of eigfact and factorization destructuring
+export eig
+function eig(A::Union{Number, StridedMatrix}; permute::Bool=true, scale::Bool=true)
+ depwarn(string("`eig(A[, permute, scale])` has been deprecated in favor of ",
+ "`eigen(A[, permute, scale])`. Whereas `eig(A[, permute, scale])` ",
+ "returns a tuple of arrays, `eigen(A[, permute, scale])` returns ",
+ "an `Eigen` object. So for a direct replacement, use ",
+ "`(eigen(A[, permute, scale])...,)`. But going forward, consider ",
+ "using the direct result of `eigen(A[, permute, scale])` instead, ",
+ "either destructured into its components ",
+ "(`vals, vecs = eigen(A[, permute, scale])`) ",
+ "or as an `Eigen` object (`X = eigen(A[, permute, scale])`)."), :eig)
+ return (eigen(A; permute=permute, scale=scale)...,)
+end
+function eig(A::AbstractMatrix, args...)
+ depwarn(string("`eig(A, args...)` has been deprecated in favor of ",
+ "`eigen(A, args...)`. Whereas `eig(A, args....)` ",
+ "returns a tuple of arrays, `eigen(A, args...)` returns ",
+ "an `Eigen` object. So for a direct replacement, use ",
+ "`(eigen(A, args...)...,)`. But going forward, consider ",
+ "using the direct result of `eigen(A, args...)` instead, ",
+ "either destructured into its components ",
+ "(`vals, vecs = eigen(A, args...)`) ",
+ "or as an `Eigen` object (`X = eigen(A, args...)`)."), :eig)
+ return (eigen(A, args...)...,)
+end
+eig(A::AbstractMatrix, B::AbstractMatrix) = _geneig(A, B)
+eig(A::Number, B::Number) = _geneig(A, B)
+function _geneig(A, B)
+ depwarn(string("`eig(A::AbstractMatrix, B::AbstractMatrix)` and ",
+ "`eig(A::Number, B::Number)` have been deprecated in favor of ",
+ "`eigen(A, B)`. Whereas the former each return a tuple of arrays, ",
+ "the latter returns a `GeneralizedEigen` object. So for a direct ",
+ "replacement, use `(eigen(A, B)...,)`. But going forward, consider ",
+ "using the direct result of `eigen(A, B)` instead, either ",
+ "destructured into its components (`vals, vecs = eigen(A, B)`), ",
+ "or as a `GeneralizedEigen` object (`X = eigen(A, B)`)."), :eig)
+ return (eigen(A, B)...,)
+end

stdlib/LinearAlgebra/src/eigen.jl

@@ -126,38 +126,6 @@ function eigen(A::StridedMatrix{T}; permute::Bool=true, scale::Bool=true) where
 end
 eigen(x::Number) = Eigen([x], fill(one(x), 1, 1))
 
-function eig(A::Union{Number, StridedMatrix}; permute::Bool=true, scale::Bool=true)
- F = eigen(A, permute=permute, scale=scale)
- F.values, F.vectors
-end
-
-"""
- eig(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) -> D, V
- eig(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) -> D, V
- eig(A, permute::Bool=true, scale::Bool=true) -> D, V
-
-Computes eigenvalues (`D`) and eigenvectors (`V`) of `A`.
-See [`eigen`](@ref) for details on the
-`irange`, `vl`, and `vu` arguments
-(for [`SymTridiagonal`](@ref), [`Hermitian`](@ref), and
-[`Symmetric`](@ref) matrices)
-and the `permute` and `scale` keyword arguments.
-The eigenvectors are returned columnwise.
-
-# Examples
-```jldoctest
-julia> eig([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0])
-([1.0, 3.0, 18.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0])
-```
-
-`eig` is a wrapper around [`eigen`](@ref), extracting all parts of the
-factorization to a tuple; where possible, using [`eigen`](@ref) is recommended.
-"""
-function eig(A::AbstractMatrix, args...)
- F = eigen(A, args...)
- F.values, F.vectors
-end
-
 """
  eigvecs(A; permute::Bool=true, scale::Bool=true) -> Matrix
 
@@ -416,39 +384,6 @@ end
 
 eigen(A::Number, B::Number) = eigen(fill(A,1,1), fill(B,1,1))
 
-"""
- eig(A, B) -> D, V
-
-Computes generalized eigenvalues (`D`) and vectors (`V`) of `A` with respect to `B`.
-
-`eig` is a wrapper around [`eigen`](@ref), extracting all parts of the
-factorization to a tuple; where possible, using [`eigen`](@ref) is recommended.
-
-# Examples
-```jldoctest
-julia> A = [1 0; 0 -1]
-2×2 Array{Int64,2}:
- 1 0
- 0 -1
-
-julia> B = [0 1; 1 0]
-2×2 Array{Int64,2}:
- 0 1
- 1 0
-
-julia> eig(A, B)
-(Complex{Float64}[0.0+1.0im, 0.0-1.0im], Complex{Float64}[0.0-1.0im 0.0+1.0im; -1.0-0.0im -1.0+0.0im])
-```
-"""
-function eig(A::AbstractMatrix, B::AbstractMatrix)
- F = eigen(A,B)
- F.values, F.vectors
-end
-function eig(A::Number, B::Number)
- F = eigen(A,B)
- F.values, F.vectors
-end
-
 """
  eigvals!(A, B) -> values
 

stdlib/LinearAlgebra/src/triangular.jl

@@ -2156,7 +2156,7 @@ function log(A0::UpperTriangular{T}) where T<:BlasFloat
  R[i,i+1] = i / sqrt((2 * i)^2 - 1)
  R[i+1,i] = R[i,i+1]
  end
- x,V = eig(R)
+ x,V = eigen(R)
  w = Vector{Float64}(undef, m)
  for i = 1:m
  x[i] = (x[i] + 1) / 2

stdlib/LinearAlgebra/test/bidiag.jl

@@ -231,8 +231,8 @@ srand(1)
 
  @testset "Eigensystems" begin
  if relty <: AbstractFloat
- d1, v1 = eig(T)
- d2, v2 = eig(map(elty<:Complex ? ComplexF64 : Float64,Tfull))
+ d1, v1 = eigen(T)
+ d2, v2 = eigen(map(elty<:Complex ? ComplexF64 : Float64,Tfull))
  @test (uplo == :U ? d1 : reverse(d1)) ≈ d2
  if elty <: Real
  test_approx_eq_modphase(v1, uplo == :U ? v2 : v2[:,n:-1:1])

stdlib/LinearAlgebra/test/eigen.jl

@@ -28,12 +28,12 @@ aimg = randn(n,n)/2
 
  α = rand(eltya)
  β = rand(eltya)
- eab = eig(α,β)
- @test eab[1] == eigvals(fill(α,1,1),fill(β,1,1))
- @test eab[2] == eigvecs(fill(α,1,1),fill(β,1,1))
+ eab = eigen(α,β)
+ @test eab.values == eigvals(fill(α,1,1),fill(β,1,1))
+ @test eab.vectors == eigvecs(fill(α,1,1),fill(β,1,1))
 
  @testset "non-symmetric eigen decomposition" begin
- d, v = eig(a)
+ d, v = eigen(a)
  for i in 1:size(a,2)
  @test a*v[:,i] ≈ d[i]*v[:,i]
  end
@@ -70,7 +70,7 @@ aimg = randn(n,n)/2
  @test eigvecs(f) === f.vectors
  @test_throws ErrorException f.Z
 
- d,v = eig(asym_sg, a_sg'a_sg)
+ d,v = eigen(asym_sg, a_sg'a_sg)
  @test d == f.values
  @test v == f.vectors
  end
@@ -89,7 +89,7 @@ aimg = randn(n,n)/2
  @test eigvecs(a1_nsg, a2_nsg) == f.vectors
  @test_throws ErrorException f.Z
 
- d,v = eig(a1_nsg, a2_nsg)
+ d,v = eigen(a1_nsg, a2_nsg)
  @test d == f.values
  @test v == f.vectors
  end
@@ -98,11 +98,11 @@ end
 
 @testset "eigenvalue computations with NaNs" begin
  for eltya in (NaN16, NaN32, NaN)
- @test_throws(ArgumentError, eig(fill(eltya, 1, 1)))
- @test_throws(ArgumentError, eig(fill(eltya, 2, 2)))
+ @test_throws(ArgumentError, eigen(fill(eltya, 1, 1)))
+ @test_throws(ArgumentError, eigen(fill(eltya, 2, 2)))
  test_matrix = rand(typeof(eltya),3,3)
  test_matrix[2,2] = eltya
- @test_throws(ArgumentError, eig(test_matrix))
+ @test_throws(ArgumentError, eigen(test_matrix))
  end
 end
 

stdlib/LinearAlgebra/test/lu.jl

@@ -51,7 +51,6 @@ dimg = randn(n)/2
  -eps(real(one(eltya)))/4 eps(real(one(eltya)))/2 -1.0 0;
  -0.5 -0.5 0.1 1.0])
  F = eigen(A, permute=false, scale=false)
- eig(A, permute=false, scale=false)
  @test F.vectors*Diagonal(F.values)/F.vectors ≈ A
  F = eigen(A)
  # @test norm(F.vectors*Diagonal(F.values)/F.vectors - A) > 0.01

stdlib/LinearAlgebra/test/schur.jl

@@ -26,7 +26,7 @@ aimg = randn(n,n)/2
  view(apd, 1:n, 1:n)))
  ε = εa = eps(abs(float(one(eltya))))
 
- d,v = eig(a)
+ d,v = eigen(a)
  f = schur(a)
  @test f.vectors*f.Schur*f.vectors' ≈ a
  @test sort(real(f.values)) ≈ sort(real(d))

stdlib/LinearAlgebra/test/symmetric.jl

@@ -218,29 +218,25 @@ end
 
  @testset "symmetric eigendecomposition" begin
  if eltya <: Real # the eigenvalues are only real and ordered for Hermitian matrices
- d, v = eig(asym)
+ d, v = eigen(asym)
  @test asym*v[:,1] ≈ d[1]*v[:,1]
  @test v*Diagonal(d)*transpose(v) ≈ asym
  @test isequal(eigvals(asym[1]), eigvals(asym[1:1,1:1]))
  @test abs.(eigen(Symmetric(asym), 1:2).vectors'v[:,1:2]) ≈ Matrix(I, 2, 2)
- eig(Symmetric(asym), 1:2) # same result, but checks that method works
  @test abs.(eigen(Symmetric(asym), d[1] - 1, (d[2] + d[3])/2).vectors'v[:,1:2]) ≈ Matrix(I, 2, 2)
- eig(Symmetric(asym), d[1] - 1, (d[2] + d[3])/2) # same result, but checks that method works
  @test eigvals(Symmetric(asym), 1:2) ≈ d[1:2]
  @test eigvals(Symmetric(asym), d[1] - 1, (d[2] + d[3])/2) ≈ d[1:2]
  # eigen doesn't support Symmetric{Complex}
  @test Matrix(eigen(asym)) ≈ asym
  @test eigvecs(Symmetric(asym)) ≈ eigvecs(asym)
  end
 
- d, v = eig(aherm)
+ d, v = eigen(aherm)
  @test aherm*v[:,1] ≈ d[1]*v[:,1]
  @test v*Diagonal(d)*v' ≈ aherm
  @test isequal(eigvals(aherm[1]), eigvals(aherm[1:1,1:1]))
  @test abs.(eigen(Hermitian(aherm), 1:2).vectors'v[:,1:2]) ≈ Matrix(I, 2, 2)
- eig(Hermitian(aherm), 1:2) # same result, but checks that method works
  @test abs.(eigen(Hermitian(aherm), d[1] - 1, (d[2] + d[3])/2).vectors'v[:,1:2]) ≈ Matrix(I, 2, 2)
- eig(Hermitian(aherm), d[1] - 1, (d[2] + d[3])/2) # same result, but checks that method works
  @test eigvals(Hermitian(aherm), 1:2) ≈ d[1:2]
  @test eigvals(Hermitian(aherm), d[1] - 1, (d[2] + d[3])/2) ≈ d[1:2]
  @test Matrix(eigen(aherm)) ≈ aherm
@@ -365,7 +361,7 @@ end
 end
 
 @testset "Issues #8057 and #8058. f=$f, A=$A" for f in
- (eigen, eigvals, eig),
+ (eigen, eigvals),
  A in (Symmetric([0 1; 1 0]), Hermitian([0 im; -im 0]))
  @test_throws ArgumentError f(A, 3, 2)
  @test_throws ArgumentError f(A, 1:4)

stdlib/LinearAlgebra/test/triangular.jl

@@ -249,7 +249,7 @@ for elty1 in (Float32, Float64, BigFloat, ComplexF32, ComplexF64, Complex{BigFlo
 
  # eigenproblems
  if !(elty1 in (BigFloat, Complex{BigFloat})) # Not handled yet
- vals, vecs = eig(A1)
+ vals, vecs = eigen(A1)
  if (t1 == UpperTriangular || t1 == LowerTriangular) && elty1 != Int # Cannot really handle degenerate eigen space and Int matrices will probably have repeated eigenvalues.
  @test vecs*diagm(0 => vals)/vecs ≈ A1 atol=sqrt(eps(float(real(one(vals[1])))))*(norm(A1,Inf)*n)^2
  end

stdlib/LinearAlgebra/test/tridiag.jl

@@ -243,7 +243,7 @@ end
  w, iblock, isplit = LAPACK.stebz!('V', 'B', -infinity, infinity, 0, 0, zero, b, a)
  evecs = LAPACK.stein!(b, a, w)
 
- (e, v) = eig(SymTridiagonal(b, a))
+ (e, v) = eigen(SymTridiagonal(b, a))
  @test e ≈ w
  test_approx_eq_vecs(v, evecs)
  end
@@ -266,10 +266,10 @@ end
  end
  @testset "eigenvalues/eigenvectors of symmetric tridiagonal" begin
  if elty === Float32 || elty === Float64
- DT, VT = @inferred eig(A)
- @inferred eig(A, 2:4)
- @inferred eig(A, 1.0, 2.0)
- D, Vecs = eig(fA)
+ DT, VT = @inferred eigen(A)
+ @inferred eigen(A, 2:4)
+ @inferred eigen(A, 1.0, 2.0)
+ D, Vecs = eigen(fA)
  @test DT ≈ D
  @test abs.(VT'Vecs) ≈ Matrix(elty(1)I, n, n)
  test_approx_eq_modphase(eigvecs(A), eigvecs(fA))

stdlib/SparseArrays/src/SparseArrays.jl

@@ -12,7 +12,7 @@ using Base.Sort: Forward
 using LinearAlgebra
 
 import Base: +, -, *, \, /, &, |, xor, ==
-import LinearAlgebra: mul!, ldiv!, rdiv!, chol, adjoint!, diag, dot, eig,
+import LinearAlgebra: mul!, ldiv!, rdiv!, chol, adjoint!, diag, dot, eigen,
  issymmetric, istril, istriu, lu, tr, transpose!, tril!, triu!,
  vecnorm, cond, diagm, factorize, ishermitian, norm, lmul!, rmul!, tril, triu
 

stdlib/SparseArrays/src/linalg.jl

@@ -1009,7 +1009,7 @@ function factorize(A::LinearAlgebra.RealHermSymComplexHerm{Float64,<:SparseMatri
 end
 
 chol(A::SparseMatrixCSC) = error("Use cholesky() instead of chol() for sparse matrices.")
-eig(A::SparseMatrixCSC) = error("Use IterativeEigensolvers.eigs() instead of eig() for sparse matrices.")
+eigen(A::SparseMatrixCSC) = error("Use IterativeEigensolvers.eigs() instead of eigen() for sparse matrices.")
 
 function Base.cov(X::SparseMatrixCSC; dims::Int=1, corrected::Bool=true)
  vardim = dims

stdlib/SparseArrays/test/sparse.jl

@@ -1780,7 +1780,7 @@ end
  C, b = A[:, 1:4], fill(1., size(A, 1))
  @test !Base.USE_GPL_LIBS || factorize(C)\b ≈ Array(C)\b
  @test_throws ErrorException chol(A)
- @test_throws ErrorException eig(A)
+ @test_throws ErrorException eigen(A)
  @test_throws ErrorException inv(A)
 end