Package evaluation to test LFAToolkit on Julia 1.14.0-DEV.2186 (48ff18f4cd*) started at 2026-05-17T15:49:15.431


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...
  Activating project at `~/.julia/environments/v1.14`

Set-up completed after 16.39s


################################################################################
# Installation
#

Installing LFAToolkit...
   Resolving package versions...
   Installed OpenSpecFun_jll ───── v0.5.6+0
   Installed LogExpFunctions ───── v0.3.29
   Installed Setfield ──────────── v1.1.2
   Installed SpecialFunctions ──── v2.7.2
   Installed MacroTools ────────── v0.5.16
   Installed Preferences ───────── v1.5.2
   Installed PrecompileTools ───── v1.3.4
   Installed ConstructionBase ──── v1.6.0
   Installed JLLWrappers ───────── v1.8.0
   Installed StaticArraysCore ──── v1.4.4
   Installed IrrationalConstants ─ v0.2.6
   Installed FastGaussQuadrature ─ v1.2.0
   Installed LFAToolkit ────────── v0.7.0
   Installed DocStringExtensions ─ v0.9.5
   Installed Polynomials ───────── v4.1.1
   Installed StaticArrays ──────── v1.9.18
   Installed OrderedCollections ── v1.8.1
  Installing 1 artifacts
   Installed artifact OpenSpecFun 194.9 KiB
    Updating `~/.julia/environments/v1.14/Project.toml`
  [3f92b583] + LFAToolkit v0.7.0
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [187b0558] + ConstructionBase v1.6.0
  [ffbed154] + DocStringExtensions v0.9.5
  [442a2c76] + FastGaussQuadrature v1.2.0
  [92d709cd] + IrrationalConstants v0.2.6
  [692b3bcd] + JLLWrappers v1.8.0
  [3f92b583] + LFAToolkit v0.7.0
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [1914dd2f] + MacroTools v0.5.16
  [bac558e1] + OrderedCollections v1.8.1
  [f27b6e38] + Polynomials v4.1.1
  [aea7be01] + PrecompileTools v1.3.4
  [21216c6a] + Preferences v1.5.2
  [efcf1570] + Setfield v1.1.2
  [276daf66] + SpecialFunctions v2.7.2
  [90137ffa] + StaticArrays v1.9.18
  [1e83bf80] + StaticArraysCore v1.4.4
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [56f22d72] + Artifacts v1.11.0
  [ade2ca70] + Dates v1.11.0
  [9fa8497b] + Future v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.13.0
  [9e88b42a] + Serialization v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [fa267f1f] + TOML v1.0.3
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.5.1+0
  [4536629a] + OpenBLAS_jll v0.3.33+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0

Installation completed after 8.39s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...
Precompiling project...
   4.6 s  ✓ TestEnv
  1 dependency successfully precompiled in 5 seconds. 27 already precompiled.

Precompiling package dependencies...
Precompiling project...
   4.4 s  ✓ MacroTools
   1.1 s  ✓ ANSIColoredPrinters
   1.3 s  ✓ ConstructionBase
   2.2 s  ✓ IrrationalConstants
   1.1 s  ✓ StaticArraysCore
   1.0 s  ✓ LazilyInitializedFields
   1.8 s  ✓ OrderedCollections
   1.4 s  ✓ DocStringExtensions
   1.2 s  ✓ AbstractTrees
   1.4 s  ✓ TranscodingStreams
   0.9 s  ✓ IOCapture
   1.9 s  ✓ StructUtils
   1.6 s  ✓ Preferences
   1.0 s  ✓ ConstructionBase → ConstructionBaseLinearAlgebraExt
   5.4 s  ✓ RegistryInstances
   1.4 s  ✓ LogExpFunctions
   1.9 s  ✓ MarkdownAST
   1.1 s  ✓ CodecZlib
   1.3 s  ✓ StructUtils → StructUtilsStaticArraysCoreExt
   2.3 s  ✓ JLLWrappers
   1.9 s  ✓ PrecompileTools
   3.1 s  ✓ Setfield
   2.4 s  ✓ Libiconv_jll
   2.4 s  ✓ Git_LFS_jll
   2.6 s  ✓ OpenSSH_jll
   2.5 s  ✓ OpenSpecFun_jll
   2.3 s  ✓ Expat_jll
  14.7 s  ✓ StaticArrays
  17.7 s  ✓ Parsers
  31.9 s  ✓ Polynomials
   4.4 s  ✓ SpecialFunctions
   1.7 s  ✓ Git_jll
   1.3 s  ✓ ConstructionBase → ConstructionBaseStaticArraysExt
   6.4 s  ✓ JSON
   5.4 s  ✓ FastGaussQuadrature
   1.4 s  ✓ Git
   6.2 s  ✓ LFAToolkit
  50.3 s  ✓ Documenter
  38 dependencies successfully precompiled in 194 seconds. 36 already precompiled.

Precompilation completed after 233.38s


################################################################################
# Testing
#

     Testing LFAToolkit
      Status `/tmp/jl_hanvki/Project.toml`
  [e30172f5] Documenter v1.17.0
  [442a2c76] FastGaussQuadrature v1.2.0
  [3f92b583] LFAToolkit v0.7.0
  [f27b6e38] Polynomials v4.1.1
  [37e2e46d] LinearAlgebra v1.13.0
  [de0858da] Printf v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_hanvki/Manifest.toml`
  [a4c015fc] ANSIColoredPrinters v0.0.1
  [1520ce14] AbstractTrees v0.4.5
  [944b1d66] CodecZlib v0.7.8
  [187b0558] ConstructionBase v1.6.0
  [ffbed154] DocStringExtensions v0.9.5
  [e30172f5] Documenter v1.17.0
  [442a2c76] FastGaussQuadrature v1.2.0
  [d7ba0133] Git v1.5.0
  [b5f81e59] IOCapture v1.0.0
  [92d709cd] IrrationalConstants v0.2.6
  [692b3bcd] JLLWrappers v1.8.0
  [682c06a0] JSON v1.6.0
  [3f92b583] LFAToolkit v0.7.0
  [0e77f7df] LazilyInitializedFields v1.3.0
  [2ab3a3ac] LogExpFunctions v0.3.29
  [1914dd2f] MacroTools v0.5.16
  [d0879d2d] MarkdownAST v0.1.3
  [bac558e1] OrderedCollections v1.8.1
  [69de0a69] Parsers v2.8.4
  [f27b6e38] Polynomials v4.1.1
  [aea7be01] PrecompileTools v1.3.4
  [21216c6a] Preferences v1.5.2
  [2792f1a3] RegistryInstances v0.1.0
  [efcf1570] Setfield v1.1.2
  [276daf66] SpecialFunctions v2.7.2
  [90137ffa] StaticArrays v1.9.18
  [1e83bf80] StaticArraysCore v1.4.4
  [ec057cc2] StructUtils v2.8.2
  [3bb67fe8] TranscodingStreams v0.11.3
  [2e619515] Expat_jll v2.8.0+0
  [020c3dae] Git_LFS_jll v3.7.1+0
  [f8c6e375] Git_jll v2.54.0+0
  [94ce4f54] Libiconv_jll v1.18.0+0
  [9bd350c2] OpenSSH_jll v10.3.1+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [9fa8497b] Future v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.13.0
  [b27032c2] LibCURL v1.0.0
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.14.0
  [de0858da] Printf v1.11.0
  [3fa0cd96] REPL v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v1.13.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.13.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.5.1+0
  [deac9b47] LibCURL_jll v8.20.0+1
  [e37daf67] LibGit2_jll v1.9.3+0
  [29816b5a] LibSSH2_jll v1.11.101+0
  [14a3606d] MozillaCACerts_jll v2026.3.19
  [4536629a] OpenBLAS_jll v0.3.33+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.6+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.2+0
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.69.0+0
  [3f19e933] p7zip_jll v17.8.0+0
     Testing Running tests...
┌ Warning: Unable to determine HTML(edit_link = ...) from remote HEAD branch, defaulting to "master".
│ Calling `git remote` failed with an exception. Set JULIA_DEBUG=Documenter to see the error.
│ Unless this is due to a configuration error, the relevant variable should be set explicitly.
└ @ Documenter ~/.julia/packages/Documenter/AXNMp/src/utilities/utilities.jl:719
[ Info: SetupBuildDirectory: setting up build directory.
[ Info: Doctest: running doctests.
┌ Error: doctest failure in ../src/Utilities.jl:43-76
│ 
│ ```jldoctest
│ numbersteps1d = 5;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # compute symbols
│     (_, eigenvalues, _) = computesymbolsoverrange(diffusion, numbersteps1d)
│ 
│     # verify
│     eigenvalues = real(eigenvalues)
│     if dimension == 1
│         @assert minimum(eigenvalues[4, :]) ≈ 1
│         @assert maximum(eigenvalues[4, :]) ≈ 4 / 3
│     elseif dimension == 2
│         @assert minimum(eigenvalues[19, :]) ≈ 2 / 3
│         @assert maximum(eigenvalues[19, :]) ≈ 64 / 45
│     elseif dimension == 3
│         @assert minimum(eigenvalues[94, :]) ≈ 1 / 3
│         @assert maximum(eigenvalues[94, :]) ≈ 256 / 225
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ numbersteps1d = 5;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # compute symbols
│     (_, eigenvalues, _) = computesymbolsoverrange(diffusion, numbersteps1d)
│ 
│     # verify
│     eigenvalues = real(eigenvalues)
│     if dimension == 1
│         @assert minimum(eigenvalues[4, :]) ≈ 1
│         @assert maximum(eigenvalues[4, :]) ≈ 4 / 3
│     elseif dimension == 2
│         @assert minimum(eigenvalues[19, :]) ≈ 2 / 3
│         @assert maximum(eigenvalues[19, :]) ≈ 64 / 45
│     elseif dimension == 3
│         @assert minimum(eigenvalues[94, :]) ≈ 1 / 3
│         @assert maximum(eigenvalues[94, :]) ≈ 256 / 225
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: eigen(A) not supported for sparse matrices. Use for example eigs(A) from the Arpack package instead.
│ Stacktrace:
│  [1] error(s::String)
│    @ Base ./error.jl:56
│  [2] eigen(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2419
│  [3] computesymbolsoverrange(operator::Operator, numbersteps1d::Int64; mass::Nothing, θ_min::Float64, θ_band::Float64)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:134
│  [4] computesymbolsoverrange
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:100 [inlined]
│  [5] top-level scope
│    @ ./none:15
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: eigen(A) not supported for sparse matrices. Use for example eigs(A) from the Arpack package instead.
│    Stacktrace:
│     [1] error(s::String)
│       @ Base ./error.jl:56
│     [2] eigen(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2419
│     [3] computesymbolsoverrange(operator::Operator, numbersteps1d::Int64; mass::Nothing, θ_min::Float64, θ_band::Float64)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:134
│     [4] computesymbolsoverrange
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:100 [inlined]
│     [5] top-level scope
│       @ ./none:15
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:43
┌ Error: doctest failure in ../src/Utilities.jl:79-97
│ 
│ ```jldoctest
│ # setup
│ numbersteps1d = 3;
│ mesh = Mesh2D(0.5, 0.5);
│ p = 6;
│ 
│ # operators
│ mass = GalleryOperator("mass", p + 1, p + 1, mesh);
│ diffusion = GalleryOperator("diffusion", p + 1, p + 1, mesh);
│ 
│ # compute symbols
│ (_, eigenvalues, _) =
│     computesymbolsoverrange(diffusion, numbersteps1d; mass = mass, θ_min = -π);
│ eigenvalues = real(eigenvalues);
│ 
│ @assert minimum(eigenvalues[2, :]) ≈ π^2
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ # setup
│ numbersteps1d = 3;
│ mesh = Mesh2D(0.5, 0.5);
│ p = 6;
│ 
│ # operators
│ mass = GalleryOperator("mass", p + 1, p + 1, mesh);
│ diffusion = GalleryOperator("diffusion", p + 1, p + 1, mesh);
│ 
│ # compute symbols
│ (_, eigenvalues, _) =
│     computesymbolsoverrange(diffusion, numbersteps1d; mass = mass, θ_min = -π);
│ eigenvalues = real(eigenvalues);
│ 
│ @assert minimum(eigenvalues[2, :]) ≈ π^2
│ 
│ Evaluated output:
│ 
│ ERROR: MethodError: no method matching ldiv!(::SparseArrays.UMFPACK.UmfpackLU{ComplexF64, Int64}, ::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `ldiv!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   ldiv!(!Matched::LinearAlgebra.UniformScaling, ::AbstractVecOrMat)
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/uniformscaling.jl:325
│   ldiv!(!Matched::LinearAlgebra.SymTridiagonal, ::AbstractVecOrMat; shift)
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/tridiag.jl:279
│   ldiv!(!Matched::Number, ::AbstractArray)
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:390
│   ...
│ 
│ Stacktrace:
│  [1] \(F::SparseArrays.UMFPACK.UmfpackLU{ComplexF64, Int64}, B::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/factorization.jl:139
│  [2] \(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, B::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2347
│  [3] computesymbolsoverrange(operator::Operator, numbersteps1d::Int64; mass::Operator, θ_min::Float64, θ_band::Float64)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:132
│  [4] top-level scope
│    @ none:1
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: MethodError: no method matching ldiv!(::SparseArrays.UMFPACK.UmfpackLU{ComplexF64, Int64}, ::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `ldiv!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      ldiv!(!Matched::LinearAlgebra.UniformScaling, ::AbstractVecOrMat)
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/uniformscaling.jl:325
│      ldiv!(!Matched::LinearAlgebra.SymTridiagonal, ::AbstractVecOrMat; shift)
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/tridiag.jl:279
│      ldiv!(!Matched::Number, ::AbstractArray)
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:390
│      ...
│    
│    Stacktrace:
│     [1] \(F::SparseArrays.UMFPACK.UmfpackLU{ComplexF64, Int64}, B::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/factorization.jl:139
│     [2] \(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, B::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2347
│     [3] computesymbolsoverrange(operator::Operator, numbersteps1d::Int64; mass::Operator, θ_min::Float64, θ_band::Float64)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:132
│     [4] top-level scope
│       @ none:1
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:79
┌ Error: doctest failure in ../src/Utilities.jl:180-216
│ 
│ ```jldoctest
│ numbersteps1d = 5;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # preconditioner
│     chebyshev = Chebyshev(diffusion)
│ 
│     # compute symbols
│     (_, eigenvalues, _) = computesymbolsoverrange(chebyshev, [1], numbersteps1d)
│ 
│     # verify
│     eigenvalues = real(eigenvalues)
│     if dimension == 1
│         @assert minimum(eigenvalues[4, :]) ≈ 0.15151515151515105
│         @assert maximum(eigenvalues[4, :]) ≈ 0.27272727272727226
│     elseif dimension == 2
│         @assert minimum(eigenvalues[19, :]) ≈ -0.25495098334134725
│         @assert maximum(eigenvalues[19, :]) ≈ -0.17128758445192374
│     elseif dimension == 3
│         @assert minimum(eigenvalues[94, :]) ≈ -0.8181818181818181
│         @assert maximum(eigenvalues[94, :]) ≈ -0.357575757575757
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ numbersteps1d = 5;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # preconditioner
│     chebyshev = Chebyshev(diffusion)
│ 
│     # compute symbols
│     (_, eigenvalues, _) = computesymbolsoverrange(chebyshev, [1], numbersteps1d)
│ 
│     # verify
│     eigenvalues = real(eigenvalues)
│     if dimension == 1
│         @assert minimum(eigenvalues[4, :]) ≈ 0.15151515151515105
│         @assert maximum(eigenvalues[4, :]) ≈ 0.27272727272727226
│     elseif dimension == 2
│         @assert minimum(eigenvalues[19, :]) ≈ -0.25495098334134725
│         @assert maximum(eigenvalues[19, :]) ≈ -0.17128758445192374
│     elseif dimension == 3
│         @assert minimum(eigenvalues[94, :]) ≈ -0.8181818181818181
│         @assert maximum(eigenvalues[94, :]) ≈ -0.357575757575757
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] geteigenvalueestimates(chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│  [4] getproperty
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│  [5] computesymbols(chebyshev::Chebyshev, ω::Vector{Int64}, θ::Array{Float64, 0})
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:581
│  [6] computesymbolsoverrange(preconditioner::Chebyshev, ω::Vector{Int64}, numbersteps1d::Int64; mass::Nothing, θ_min::Float64, θ_band::Float64)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:250
│  [7] computesymbolsoverrange
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:219 [inlined]
│  [8] top-level scope
│    @ ./none:18
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] geteigenvalueestimates(chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│     [4] getproperty
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│     [5] computesymbols(chebyshev::Chebyshev, ω::Vector{Int64}, θ::Array{Float64, 0})
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:581
│     [6] computesymbolsoverrange(preconditioner::Chebyshev, ω::Vector{Int64}, numbersteps1d::Int64; mass::Nothing, θ_min::Float64, θ_band::Float64)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:250
│     [7] computesymbolsoverrange
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:219 [inlined]
│     [8] top-level scope
│       @ ./none:18
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:180
┌ Error: doctest failure in ../src/Utilities.jl:302-342
│ 
│ ```jldoctest
│ numbersteps1d = 5;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     ctofbasis = TensorH1LagrangeBasis(3, 5, 1, dimension; collocatedquadrature = true)
│ 
│     # operators
│     finediffusion = GalleryOperator("diffusion", 5, 5, mesh)
│     coarsediffusion = GalleryOperator("diffusion", 3, 5, mesh)
│ 
│     # smoother
│     jacobi = Jacobi(finediffusion)
│ 
│     # preconditioner
│     multigrid = PMultigrid(finediffusion, coarsediffusion, jacobi, [ctofbasis])
│ 
│     # compute symbols
│     (_, eigenvalues, _) = computesymbolsoverrange(multigrid, [1.0], [1, 1], numbersteps1d)
│ 
│     # verify
│     eigenvalues = real(eigenvalues)
│     if dimension == 1
│         @assert maximum(eigenvalues[4, :]) ≈ 0.64
│     elseif dimension == 2
│         @assert maximum(eigenvalues[19, :]) ≈ 0.9082562365654528
│     elseif dimension == 3
│         @assert maximum(eigenvalues[94, :]) ≈ 1.4359882222222669
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ numbersteps1d = 5;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     ctofbasis = TensorH1LagrangeBasis(3, 5, 1, dimension; collocatedquadrature = true)
│ 
│     # operators
│     finediffusion = GalleryOperator("diffusion", 5, 5, mesh)
│     coarsediffusion = GalleryOperator("diffusion", 3, 5, mesh)
│ 
│     # smoother
│     jacobi = Jacobi(finediffusion)
│ 
│     # preconditioner
│     multigrid = PMultigrid(finediffusion, coarsediffusion, jacobi, [ctofbasis])
│ 
│     # compute symbols
│     (_, eigenvalues, _) = computesymbolsoverrange(multigrid, [1.0], [1, 1], numbersteps1d)
│ 
│     # verify
│     eigenvalues = real(eigenvalues)
│     if dimension == 1
│         @assert maximum(eigenvalues[4, :]) ≈ 0.64
│     elseif dimension == 2
│         @assert maximum(eigenvalues[19, :]) ≈ 0.9082562365654528
│     elseif dimension == 3
│         @assert maximum(eigenvalues[94, :]) ≈ 1.4359882222222669
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│ Stacktrace:
│  [1] error(s::String)
│    @ Base ./error.jl:56
│  [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│  [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│  [4] computesymbols(multigrid::Multigrid, p::Vector{Float64}, v::Vector{Int64}, θ::Array{Float64, 0})
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Multigrid/Base.jl:360
│  [5] computesymbolsoverrange(multigrid::Multigrid, p::Vector{Float64}, v::Vector{Int64}, numbersteps1d::Int64; mass::Nothing, θ_min::Float64, θ_band::Float64)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:377
│  [6] computesymbolsoverrange
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:345 [inlined]
│  [7] top-level scope
│    @ ./none:25
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│    Stacktrace:
│     [1] error(s::String)
│       @ Base ./error.jl:56
│     [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│     [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│     [4] computesymbols(multigrid::Multigrid, p::Vector{Float64}, v::Vector{Int64}, θ::Array{Float64, 0})
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Multigrid/Base.jl:360
│     [5] computesymbolsoverrange(multigrid::Multigrid, p::Vector{Float64}, v::Vector{Int64}, numbersteps1d::Int64; mass::Nothing, θ_min::Float64, θ_band::Float64)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:377
│     [6] computesymbolsoverrange
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:345 [inlined]
│     [7] top-level scope
│       @ ./none:25
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/Utilities.jl:302
┌ Error: doctest failure in ../src/PC/Chebyshev.jl:24-49
│ 
│ ```jldoctest
│ # setup
│ mesh = Mesh2D(1.0, 1.0);
│ mass = GalleryOperator("mass", 4, 4, mesh);
│ 
│ # preconditioner
│ chebyshev = Chebyshev(mass);
│ 
│ # verify
│ println(chebyshev)
│ 
│ # output
│ 
│ chebyshev preconditioner:
│ 1st-kind Chebyshev
│ eigenvalue estimates:
│   estimated minimum 0.2500
│   estimated maximum 1.3611
│ estimate scaling:
│   λ_min = a * estimated min + b * estimated max
│   λ_max = c * estimated min + d * estimated max
│   a = 0.0000
│   b = 0.1000
│   c = 0.0000
│   d = 1.0000
│ ```
│ 
│ Subexpression:
│ 
│ # setup
│ mesh = Mesh2D(1.0, 1.0);
│ mass = GalleryOperator("mass", 4, 4, mesh);
│ 
│ # preconditioner
│ chebyshev = Chebyshev(mass);
│ 
│ # verify
│ println(chebyshev)
│ 
│ Evaluated output:
│ 
│ chebyshev preconditioner:
│ 1st-kind Chebyshev
│ eigenvalue estimates:ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] geteigenvalueestimates(chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:192
│  [4] getproperty
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│  [5] show(io::IOContext{Base.PipeEndpoint}, chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:84
│  [6] print(io::IOContext{Base.PipeEndpoint}, x::Chebyshev)
│    @ Base ./strings/io.jl:35
│  [7] print(::IOContext{Base.PipeEndpoint}, ::Chebyshev, ::String)
│    @ Base ./strings/io.jl:46
│  [8] println(x::Chebyshev)
│    @ Base ./coreio.jl:5
│  [9] top-level scope
│    @ none:1
│ 
│ Expected output:
│ 
│ chebyshev preconditioner:
│ 1st-kind Chebyshev
│ eigenvalue estimates:
│   estimated minimum 0.2500
│   estimated maximum 1.3611
│ estimate scaling:
│   λ_min = a * estimated min + b * estimated max
│   λ_max = c * estimated min + d * estimated max
│   a = 0.0000
│   b = 0.1000
│   c = 0.0000
│   d = 1.0000
│ 
│   diff =
│    Warning: Diff output requires color.
│    chebyshev preconditioner:
│    1st-kind Chebyshev
│    eigenvalue estimates:
│      estimated minimum 0.2500
│      estimated maximum 1.3611
│    estimate scaling:
│      λ_min = a * estimated min + b * estimated max
│      λ_max = c * estimated min + d * estimated max
│      a = 0.0000
│      b = 0.1000
│      c = 0.0000
│      d = 1.0000estimates:ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] geteigenvalueestimates(chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:192
│     [4] getproperty
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│     [5] show(io::IOContext{Base.PipeEndpoint}, chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:84
│     [6] print(io::IOContext{Base.PipeEndpoint}, x::Chebyshev)
│       @ Base ./strings/io.jl:35
│     [7] print(::IOContext{Base.PipeEndpoint}, ::Chebyshev, ::String)
│       @ Base ./strings/io.jl:46
│     [8] println(x::Chebyshev)
│       @ Base ./coreio.jl:5
│     [9] top-level scope
│       @ none:1
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:24
┌ Error: doctest failure in ../src/Operator/Base.jl:894-927
│ 
│ ```jldoctest
│ using LinearAlgebra;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # compute symbols
│     A = computesymbols(diffusion, π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert minimum(eigenvalues) ≈ 1
│         @assert maximum(eigenvalues) ≈ 4 / 3
│     elseif dimension == 2
│         @assert minimum(eigenvalues) ≈ 2 / 3
│         @assert maximum(eigenvalues) ≈ 64 / 45
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ 1 / 3
│         @assert maximum(eigenvalues) ≈ 256 / 225
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ using LinearAlgebra;
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # compute symbols
│     A = computesymbols(diffusion, π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert minimum(eigenvalues) ≈ 1
│         @assert maximum(eigenvalues) ≈ 4 / 3
│     elseif dimension == 2
│         @assert minimum(eigenvalues) ≈ 2 / 3
│         @assert maximum(eigenvalues) ≈ 64 / 45
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ 1 / 3
│         @assert maximum(eigenvalues) ≈ 256 / 225
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] top-level scope
│    @ ./none:18
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] top-level scope
│       @ ./none:18
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/Operator/Base.jl:894
┌ Error: doctest failure in ../src/PC/Jacobi.jl:176-209
│ 
│ ```jldoctest
│ using LinearAlgebra
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # preconditioner
│     jacobi = Jacobi(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(jacobi, [1.0], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert maximum(eigenvalues) ≈ 1 / 7
│     elseif dimension == 2
│         @assert minimum(eigenvalues) ≈ -1 / 14
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ -0.33928571428571486
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ using LinearAlgebra
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # preconditioner
│     jacobi = Jacobi(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(jacobi, [1.0], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert maximum(eigenvalues) ≈ 1 / 7
│     elseif dimension == 2
│         @assert minimum(eigenvalues) ≈ -1 / 14
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ -0.33928571428571486
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] top-level scope
│    @ ./none:21
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] top-level scope
│       @ ./none:21
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Jacobi.jl:176
┌ Error: doctest failure in ../src/PC/Chebyshev.jl:524-560
│ 
│ ```jldoctest
│ using LinearAlgebra
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # preconditioner
│     chebyshev = Chebyshev(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(chebyshev, [1], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert minimum(eigenvalues) ≈ 0.15151515151515105
│         @assert maximum(eigenvalues) ≈ 0.27272727272727226
│     elseif dimension == 2
│         @assert minimum(eigenvalues) ≈ -0.25495098334134725
│         @assert maximum(eigenvalues) ≈ -0.17128758445192374
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ -0.8181818181818181
│         @assert maximum(eigenvalues) ≈ -0.357575757575757
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ using LinearAlgebra
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│ 
│     # preconditioner
│     chebyshev = Chebyshev(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(chebyshev, [1], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert minimum(eigenvalues) ≈ 0.15151515151515105
│         @assert maximum(eigenvalues) ≈ 0.27272727272727226
│     elseif dimension == 2
│         @assert minimum(eigenvalues) ≈ -0.25495098334134725
│         @assert maximum(eigenvalues) ≈ -0.17128758445192374
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ -0.8181818181818181
│         @assert maximum(eigenvalues) ≈ -0.357575757575757
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] geteigenvalueestimates(chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│  [4] getproperty
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│  [5] computesymbols(chebyshev::Chebyshev, ω::Vector{Int64}, θ::Vector{Float64})
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:581
│  [6] top-level scope
│    @ ./none:18
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] geteigenvalueestimates(chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│     [4] getproperty
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│     [5] computesymbols(chebyshev::Chebyshev, ω::Vector{Int64}, θ::Vector{Float64})
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:581
│     [6] top-level scope
│       @ ./none:18
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:524
┌ Error: doctest failure in ../src/PC/Multigrid/Base.jl:297-337
│ 
│ ```jldoctest
│ using LinearAlgebra
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     ctofbasis = TensorH1LagrangeBasis(3, 5, 1, dimension; collocatedquadrature = true)
│ 
│     # operators
│     finediffusion = GalleryOperator("diffusion", 5, 5, mesh)
│     coarsediffusion = GalleryOperator("diffusion", 3, 5, mesh)
│ 
│     # smoother
│     jacobi = Jacobi(finediffusion)
│ 
│     # preconditioner
│     multigrid = PMultigrid(finediffusion, coarsediffusion, jacobi, [ctofbasis])
│ 
│     # compute symbols
│     A = computesymbols(multigrid, [1.0], [1, 1], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert maximum(eigenvalues) ≈ 0.64
│     elseif dimension == 2
│         @assert maximum(eigenvalues) ≈ 0.9082562365654528
│     elseif dimension == 3
│         @assert maximum(eigenvalues) ≈ 1.4359882222222669
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ using LinearAlgebra
│ 
│ for dimension = 1:3
│     # setup
│     mesh = []
│     if dimension == 1
│         mesh = Mesh1D(1.0)
│     elseif dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     ctofbasis = TensorH1LagrangeBasis(3, 5, 1, dimension; collocatedquadrature = true)
│ 
│     # operators
│     finediffusion = GalleryOperator("diffusion", 5, 5, mesh)
│     coarsediffusion = GalleryOperator("diffusion", 3, 5, mesh)
│ 
│     # smoother
│     jacobi = Jacobi(finediffusion)
│ 
│     # preconditioner
│     multigrid = PMultigrid(finediffusion, coarsediffusion, jacobi, [ctofbasis])
│ 
│     # compute symbols
│     A = computesymbols(multigrid, [1.0], [1, 1], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 1
│         @assert maximum(eigenvalues) ≈ 0.64
│     elseif dimension == 2
│         @assert maximum(eigenvalues) ≈ 0.9082562365654528
│     elseif dimension == 3
│         @assert maximum(eigenvalues) ≈ 1.4359882222222669
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│ Stacktrace:
│  [1] error(s::String)
│    @ Base ./error.jl:56
│  [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│  [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│  [4] computesymbols(multigrid::Multigrid, p::Vector{Float64}, v::Vector{Int64}, θ::Vector{Float64})
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Multigrid/Base.jl:360
│  [5] top-level scope
│    @ ./none:25
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│    Stacktrace:
│     [1] error(s::String)
│       @ Base ./error.jl:56
│     [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│     [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│     [4] computesymbols(multigrid::Multigrid, p::Vector{Float64}, v::Vector{Int64}, θ::Vector{Float64})
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Multigrid/Base.jl:360
│     [5] top-level scope
│       @ ./none:25
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Multigrid/Base.jl:297
┌ Error: doctest failure in ../src/PC/BDDC/Base.jl:1087-1116
│ 
│ ```jldoctest
│ using LinearAlgebra;
│ 
│ for dimension = 2:3
│     # setup
│     mesh = []
│     if dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│     bddc = LumpedBDDC(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(bddc, [0.2], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 2
│         @assert minimum(eigenvalues) ≈ 0.43999999999999995
│         @assert maximum(eigenvalues) ≈ 0.8
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ -0.6319999999999972
│         @assert maximum(eigenvalues) ≈ 0.8
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ using LinearAlgebra;
│ 
│ for dimension = 2:3
│     # setup
│     mesh = []
│     if dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│     bddc = LumpedBDDC(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(bddc, [0.2], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 2
│         @assert minimum(eigenvalues) ≈ 0.43999999999999995
│         @assert maximum(eigenvalues) ≈ 0.8
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ -0.6319999999999972
│         @assert maximum(eigenvalues) ≈ 0.8
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│ Stacktrace:
│  [1] error(s::String)
│    @ Base ./error.jl:56
│  [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│  [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│  [4] computesymbols(bddc::BDDC, ω::Vector{Float64}, θ::Vector{Float64})
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/BDDC/Base.jl:1214
│  [5] top-level scope
│    @ ./none:14
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│    Stacktrace:
│     [1] error(s::String)
│       @ Base ./error.jl:56
│     [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│     [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│     [4] computesymbols(bddc::BDDC, ω::Vector{Float64}, θ::Vector{Float64})
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/BDDC/Base.jl:1214
│     [5] top-level scope
│       @ ./none:14
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/BDDC/Base.jl:1087
┌ Error: doctest failure in ../src/PC/BDDC/Base.jl:1121-1150
│ 
│ ```jldoctest
│ using LinearAlgebra;
│ 
│ for dimension = 2:3
│     # setup
│     mesh = []
│     if dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│     bddc = DirichletBDDC(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(bddc, [0.2], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 2
│         @assert minimum(eigenvalues) ≈ 0.7999999999999998
│         @assert maximum(eigenvalues) ≈ 0.8
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ 0.7801226993865031
│         @assert maximum(eigenvalues) ≈ 0.8
│     end
│ end
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ using LinearAlgebra;
│ 
│ for dimension = 2:3
│     # setup
│     mesh = []
│     if dimension == 2
│         mesh = Mesh2D(1.0, 1.0)
│     elseif dimension == 3
│         mesh = Mesh3D(1.0, 1.0, 1.0)
│     end
│     diffusion = GalleryOperator("diffusion", 3, 3, mesh)
│     bddc = DirichletBDDC(diffusion)
│ 
│     # compute symbols
│     A = computesymbols(bddc, [0.2], π * ones(dimension))
│ 
│     # verify
│     eigenvalues = real(eigvals(A))
│     if dimension == 2
│         @assert minimum(eigenvalues) ≈ 0.7999999999999998
│         @assert maximum(eigenvalues) ≈ 0.8
│     elseif dimension == 3
│         @assert minimum(eigenvalues) ≈ 0.7801226993865031
│         @assert maximum(eigenvalues) ≈ 0.8
│     end
│ end
│ 
│ Evaluated output:
│ 
│ ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│ Stacktrace:
│  [1] error(s::String)
│    @ Base ./error.jl:56
│  [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│  [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│  [4] computesymbols(bddc::BDDC, ω::Vector{Float64}, θ::Vector{Float64})
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/BDDC/Base.jl:1214
│  [5] top-level scope
│    @ ./none:14
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: The inverse of a sparse matrix can often be dense and can cause the computer to run out of memory. If you are sure you have enough memory, please either convert your matrix to a dense matrix, e.g. by calling `Matrix` or if `A` can be factorized, use `\` on the dense identity matrix, e.g. `A \ Matrix{eltype(A)}(I, size(A)...)` restrictions of `\` on sparse lhs applies. Alternatively, `A\b` is generally preferable to `inv(A)*b`
│    Stacktrace:
│     [1] error(s::String)
│       @ Base ./error.jl:56
│     [2] inv(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ SparseArrays /opt/julia/share/julia/stdlib/v1.14/SparseArrays/src/linalg.jl:2038
│     [3] literal_pow(::typeof(^), A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}, ::Val{-1})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/generic.jl:1193
│     [4] computesymbols(bddc::BDDC, ω::Vector{Float64}, θ::Vector{Float64})
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/BDDC/Base.jl:1214
│     [5] top-level scope
│       @ ./none:14
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/BDDC/Base.jl:1121
┌ Error: doctest failure in ../src/PC/Chebyshev.jl:157-169
│ 
│ ```jldoctest
│ # setup
│ mesh = Mesh1D(1.0);
│ diffusion = GalleryOperator("diffusion", 3, 3, mesh);
│ 
│ # preconditioner
│ chebyshev = Chebyshev(diffusion);
│ 
│ # verify eigenvalue estimates
│ @assert chebyshev.eigenvalueestimates ≈ [0, 15 / 7]
│ 
│ # output
│ 
│ ```
│ 
│ Subexpression:
│ 
│ # setup
│ mesh = Mesh1D(1.0);
│ diffusion = GalleryOperator("diffusion", 3, 3, mesh);
│ 
│ # preconditioner
│ chebyshev = Chebyshev(diffusion);
│ 
│ # verify eigenvalue estimates
│ @assert chebyshev.eigenvalueestimates ≈ [0, 15 / 7]
│ 
│ Evaluated output:
│ 
│ ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] geteigenvalueestimates(chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│  [4] getproperty(preconditioner::Chebyshev, f::Symbol)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483
│  [5] top-level scope
│    @ none:3
│ 
│ Expected output:
│ 
│ 
│ 
│   diff =
│    Warning: Diff output requires color.
│    ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] geteigenvalueestimates(chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│     [4] getproperty(preconditioner::Chebyshev, f::Symbol)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483
│     [5] top-level scope
│       @ none:3
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:157
┌ Error: doctest failure in ../src/PC/Chebyshev.jl:231-259
│ 
│ ```jldoctest
│ # setup
│ mesh = Mesh1D(1.0);
│ diffusion = GalleryOperator("diffusion", 3, 3, mesh);
│ 
│ # preconditioner
│ chebyshev = Chebyshev(diffusion)
│ 
│ # set eigenvalue estimate scaling
│ # PETSc default is to use 1.1, 0.1 of max eigenvalue estimate
│ #   https://www.mcs.anl.gov/petsc/petsc-3.3/docs/manualpages/KSP/KSPChebyshevSetEstimateEigenvalues.html
│ chebyshev.eigenvaluescaling = [0.0, 0.1, 0.0, 1.1];
│ println(chebyshev)
│ 
│ # output
│ 
│ chebyshev preconditioner:
│ 1st-kind Chebyshev
│ eigenvalue estimates:
│   estimated minimum 0.0000
│   estimated maximum 2.1429
│ estimate scaling:
│   λ_min = a * estimated min + b * estimated max
│   λ_max = c * estimated min + d * estimated max
│   a = 0.0000
│   b = 0.1000
│   c = 0.0000
│   d = 1.1000
│ ```
│ 
│ Subexpression:
│ 
│ # setup
│ mesh = Mesh1D(1.0);
│ diffusion = GalleryOperator("diffusion", 3, 3, mesh);
│ 
│ # preconditioner
│ chebyshev = Chebyshev(diffusion)
│ 
│ # set eigenvalue estimate scaling
│ # PETSc default is to use 1.1, 0.1 of max eigenvalue estimate
│ #   https://www.mcs.anl.gov/petsc/petsc-3.3/docs/manualpages/KSP/KSPChebyshevSetEstimateEigenvalues.html
│ chebyshev.eigenvaluescaling = [0.0, 0.1, 0.0, 1.1];
│ println(chebyshev)
│ 
│ Evaluated output:
│ 
│ chebyshev preconditioner:
│ 1st-kind Chebyshev
│ eigenvalue estimates:ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│ The function `eigvals!` exists, but no method is defined for this combination of argument types.
│ 
│ Closest candidates are:
│   eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│   eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│   ...
│ 
│ Stacktrace:
│  [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│  [3] geteigenvalueestimates(chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│  [4] getproperty
│    @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│  [5] show(io::IOContext{Base.PipeEndpoint}, chebyshev::Chebyshev)
│    @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:84
│  [6] print(io::IOContext{Base.PipeEndpoint}, x::Chebyshev)
│    @ Base ./strings/io.jl:35
│  [7] print(::IOContext{Base.PipeEndpoint}, ::Chebyshev, ::String)
│    @ Base ./strings/io.jl:46
│  [8] println(x::Chebyshev)
│    @ Base ./coreio.jl:5
│  [9] top-level scope
│    @ none:1
│ 
│ Expected output:
│ 
│ chebyshev preconditioner:
│ 1st-kind Chebyshev
│ eigenvalue estimates:
│   estimated minimum 0.0000
│   estimated maximum 2.1429
│ estimate scaling:
│   λ_min = a * estimated min + b * estimated max
│   λ_max = c * estimated min + d * estimated max
│   a = 0.0000
│   b = 0.1000
│   c = 0.0000
│   d = 1.1000
│ 
│   diff =
│    Warning: Diff output requires color.
│    chebyshev preconditioner:
│    1st-kind Chebyshev
│    eigenvalue estimates:
│      estimated minimum 0.0000
│      estimated maximum 2.1429
│    estimate scaling:
│      λ_min = a * estimated min + b * estimated max
│      λ_max = c * estimated min + d * estimated max
│      a = 0.0000
│      b = 0.1000
│      c = 0.0000
│      d = 1.1000estimates:ERROR: MethodError: no method matching eigvals!(::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│    The function `eigvals!` exists, but no method is defined for this combination of argument types.
│    
│    Closest candidates are:
│      eigvals!(::AbstractMatrix{T}, !Matched::LinearAlgebra.Cholesky{T}; sortby) where T<:Number
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/symmetriceigen.jl:355
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{Float32, Float64}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:420
│      eigvals!(!Matched::LinearAlgebra.UpperHessenberg{T, <:StridedMatrix{T}}; permute, scale, sortby) where T<:Union{ComplexF64, ComplexF32}
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/hessenberg.jl:416
│      ...
│    
│    Stacktrace:
│     [1] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64}; kws::@Kwargs{})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [2] eigvals(A::SparseArrays.SparseMatrixCSC{ComplexF64, Int64})
│       @ LinearAlgebra /opt/julia/share/julia/stdlib/v1.14/LinearAlgebra/src/eigen.jl:345
│     [3] geteigenvalueestimates(chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:185
│     [4] getproperty
│       @ ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:483 [inlined]
│     [5] show(io::IOContext{Base.PipeEndpoint}, chebyshev::Chebyshev)
│       @ LFAToolkit ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:84
│     [6] print(io::IOContext{Base.PipeEndpoint}, x::Chebyshev)
│       @ Base ./strings/io.jl:35
│     [7] print(::IOContext{Base.PipeEndpoint}, ::Chebyshev, ::String)
│       @ Base ./strings/io.jl:46
│     [8] println(x::Chebyshev)
│       @ Base ./coreio.jl:5
│     [9] top-level scope
│       @ none:1
└ @ Documenter ~/.julia/packages/LFAToolkit/ImlV6/src/PC/Chebyshev.jl:231
┌ Error: Doctesting failed
│   exception =
│    `makedocs` encountered a doctest error. Terminating build
│    Stacktrace:
│      [1] error(s::String)
│        @ Base ./error.jl:56
│      [2] runner(::Type{Documenter.Builder.Doctest}, doc::Documenter.Document)
│        @ Documenter ~/.julia/packages/Documenter/AXNMp/src/builder_pipeline.jl:213
│      [3] dispatch(::Type{Documenter.Builder.DocumentPipeline}, x::Documenter.Document)
│        @ Documenter.Selectors ~/.julia/packages/Documenter/AXNMp/src/utilities/Selectors.jl:170
│      [4] delete!(::Base.EnvDict, k::String)
│        @ Base ~/.julia/packages/Documenter/AXNMp/src/makedocs.jl:283 [inlined]
│      [5] withenv(::Documenter.var"#95#96"{Documenter.Document}, ::Pair{String, Nothing}, ::Vararg{Pair{String, Nothing}})
│        @ Base ./env.jl:265
│      [6] #93
│        @ ~/.julia/packages/Documenter/AXNMp/src/makedocs.jl:282 [inlined]
│      [7] cd(f::Documenter.var"#93#94"{Documenter.Document}, dir::String)
│        @ Base.Filesystem ./file.jl:112
│      [8] makedocs(; debug::Bool, format::Documenter.HTMLWriter.HTML, kwargs::@Kwargs{root::String, source::String, sitename::String, doctest::Symbol, modules::Vector{Module}, doctestfilters::Vector{Regex}, remotes::Nothing, plugins::Vector{Documenter.Plugin}})
│        @ Documenter ~/.julia/packages/Documenter/AXNMp/src/makedocs.jl:281
│      [9] (::Documenter.var"#all_doctests#147"{Bool, Vector{Regex}, Vector{Documenter.Plugin}, Vector{Module}})()
│        @ Documenter ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:85
│     [10] macro expansion
│        @ ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:110 [inlined]
│     [11] macro expansion
│        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined]
│     [12] macro expansion
│        @ ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:110 [inlined]
│     [13] get_testset()
│        @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined]
│     [14] doctest(source::Nothing, modules::Vector{Module}; fix::Bool, testset::String, doctestfilters::Vector{Regex}, plugins::Vector{Documenter.Plugin})
│        @ Documenter ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:110
│     [15] doctest(package::Module; manual::Bool, testset::Nothing, kwargs::@Kwargs{})
│        @ Documenter ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:37
│     [16] top-level scope
│        @ ~/.julia/packages/LFAToolkit/ImlV6/test/runtests.jl:13
│     [17] macro expansion
│        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined]
│     [18] macro expansion
│        @ ~/.julia/packages/LFAToolkit/ImlV6/test/runtests.jl:13 [inlined]
│     [19] include(mapexpr::Function, mod::Module, _path::String)
│        @ Base ./Base.jl:327
│     [20] top-level scope
│        @ none:6
│     [21] eval(m::Module, e::Any)
│        @ Core ./boot.jl:517
│     [22] exec_options(opts::Base.JLOptions)
│        @ Base ./client.jl:318
│     [23] _start()
│        @ Base ./client.jl:593
└ @ Documenter ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:99

Doctests: LFAToolkit: Test Failed at /home/pkgeval/.julia/packages/Documenter/AXNMp/src/doctest.jl:110
  Expression: all_doctests()
  Stacktrace:
   [1] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:784 [inlined]
   [2] macro expansion
     @ ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:110 [inlined]
   [3] Dict{Symbol, Any}()
     @ Base /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined]
   [4] doctest(source::Nothing, modules::Vector{Module}; fix::Bool, testset::String, doctestfilters::Vector{Regex}, plugins::Vector{Documenter.Plugin})
     @ Documenter ~/.julia/packages/Documenter/AXNMp/src/doctest.jl:110
Test Summary:                                 | Fail  Total     Time
  documentation tests                         |    1      1  4m18.3s
  Doctests: LFAToolkit                        |    1      1  4m15.4s
RNG of the outermost testset: Random.Xoshiro(0xaa4b083341a45f9b, 0x236f076117a45ad9, 0x50f2166f2fe6da29, 0xd6fe6221fb73a583, 0x8a473d70583d3988)
ERROR: LoadError: Some tests did not pass: 0 passed, 1 failed, 0 errored, 0 broken.
in expression starting at /home/pkgeval/.julia/packages/LFAToolkit/ImlV6/test/runtests.jl:12

Testing failed after 283.34s

ERROR: LoadError: Package LFAToolkit errored during testing
Stacktrace:
  [1] pkgerror(msg::String)
    @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68
  [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool)
    @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3247
  [3] Cmd(cmd::Cmd)
    @ Base /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3110 [inlined]
  [4] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:587
  [5] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172
  [6] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160
  [7] test
    @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [inlined]
  [8] test(pkg::String; kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [inlined]
  [9] top-level scope
    @ /PkgEval.jl/scripts/evaluate.jl:223
 [10] include(mod::Module, _path::String)
    @ Base ./Base.jl:326
 [11] exec_options(opts::Base.JLOptions)
    @ Base ./client.jl:352
 [12] _start()
    @ Base ./client.jl:593
in expression starting at /PkgEval.jl/scripts/evaluate.jl:214

PkgEval failed after 560.47s: package has test failures