mark test

Author: ChrisRackauckas

ext/LinearSolveSparseArraysExt.jl

@@ -47,6 +47,7 @@ end
 function LinearSolve.defaultalg(A::AbstractSparseMatrixCSC{Tv, Ti}, b,
         assump::OperatorAssumptions{Bool}) where {Tv, Ti}
     ext = Base.get_extension(LinearSolve, :LinearSolveSparspakExt)
+    @show "here2", Tv, Ti, typeof(A)
     if assump.issq && ext !== nothing
         LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.SparspakFactorization)
     elseif !assump.issq
@@ -246,6 +247,7 @@ end
 function LinearSolve.defaultalg(
         A::AbstractSparseMatrixCSC{<:Union{Float64, ComplexF64}, Ti}, b,
         assump::OperatorAssumptions{Bool}) where {Ti}
+    @show "here"
     if assump.issq
         if length(b) <= 10_000 && length(nonzeros(A)) / length(A) < 2e-4
             LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.KLUFactorization)

test/default_algs.jl

@@ -147,6 +147,17 @@ sol = solve!(cache)
 
 ## Non-square Sparse Defaults 
 # https://github.com/SciML/NonlinearSolve.jl/issues/599
+A = SparseMatrixCSC{Float64, Int64}([
+    1.0 0.0
+    1.0 1.0
+])
+b = ones(2)
+A2 = hcat(A,A)
+prob = LinearProblem(A, b)
+@test SciMLBase.successful_retcode(solve(prob))
+
+prob2 = LinearProblem(A2, b)
+@test SciMLBase.successful_retcode(solve(prob2))
 
 A = SparseMatrixCSC{Float64, Int32}([
     1.0 0.0
@@ -157,5 +168,7 @@ A2 = hcat(A,A)
 prob = LinearProblem(A, b)
 @test SciMLBase.successful_retcode(solve(prob))
 
+@info "This test"
+
 prob2 = LinearProblem(A2, b)
 @test SciMLBase.successful_retcode(solve(prob2))