Replace Pinv default with BackslashSolver for better performance and Windows compatibility

src/aggregation.jl

@@ -12,7 +12,7 @@ function smoothed_aggregation(A::TA,
                         max_coarse = 10,
                         diagonal_dominance = false,
                         keep = false,
-                        coarse_solver = Pinv, kwargs...) where {T,V,bs,TA<:SparseMatrixCSC{T,V}}
+                        coarse_solver = BackslashSolver, kwargs...) where {T,V,bs,TA<:SparseMatrixCSC{T,V}}
     n = size(A, 1)
     B = isnothing(B) ? ones(T,n) : copy(B)
     @assert size(A, 1) == size(B, 1)

src/classical.jl

@@ -7,7 +7,7 @@ function ruge_stuben(_A::Union{TA, Symmetric{Ti, TA}, Hermitian{Ti, TA}},
                 postsmoother = GaussSeidel(),
                 max_levels = 10,
                 max_coarse = 10,
-                coarse_solver = Pinv, kwargs...) where {Ti,Tv,bs,TA<:SparseMatrixCSC{Ti,Tv}}
+                coarse_solver = BackslashSolver, kwargs...) where {Ti,Tv,bs,TA<:SparseMatrixCSC{Ti,Tv}}
 
 
     # fails if near null space `B` is provided

src/multilevel.jl

@@ -53,10 +53,41 @@ end
 
 abstract type CoarseSolver end
 
+"""
+    BackslashSolver{T,F} <: CoarseSolver
+
+Coarse solver using Julia's built-in factorizations via `factorize()` and `ldiv!()`.
+Much more efficient than `Pinv` as it preserves sparsity and uses appropriate 
+factorizations (UMFPACK, CHOLMOD, etc.) based on matrix properties.
+"""
+struct BackslashSolver{T,F} <: CoarseSolver
+    factorization::F
+    function BackslashSolver{T}(A) where T
+        # Use Julia's built-in factorize - automatically picks best method
+        # (UMFPACK for sparse nonsymmetric, CHOLMOD for sparse SPD, etc.)
+        fact = factorize(A)
+        new{T,typeof(fact)}(fact)
+    end
+end
+BackslashSolver(A) = BackslashSolver{eltype(A)}(A)
+Base.show(io::IO, p::BackslashSolver) = print(io, "BackslashSolver")
+
+function (solver::BackslashSolver)(x, b)
+    # Handle multiple RHS efficiently
+    for i ∈ 1:size(b, 2)
+        # Use backslash - Julia's factorizations are optimized for this
+        x[:, i] = solver.factorization \ b[:, i]
+    end
+end
+
 """
     Pinv{T} <: CoarseSolver
 
 Moore-Penrose pseudo inverse coarse solver. Calls `pinv`
+
+!!! warning "Deprecated"
+    This solver converts sparse matrices to dense and computes the full pseudoinverse,
+    which is very inefficient. Consider using `BackslashSolver` instead.
 """
 struct Pinv{T} <: CoarseSolver
     pinvA::Matrix{T}

test/runtests.jl

@@ -1,7 +1,7 @@
 using SparseArrays, DelimitedFiles, Random
 using Test, LinearAlgebra
 using IterativeSolvers, LinearSolve, AlgebraicMultigrid
-import AlgebraicMultigrid: Pinv, Classical
+import AlgebraicMultigrid: Pinv, BackslashSolver, Classical
 using JLD2
 using FileIO
 
@@ -71,7 +71,30 @@ end
 @testset "Coarse Solver" begin
 A = float.(poisson(10))
 b = A * ones(10)
+
+# Test BackslashSolver (new default, much more efficient)
+x = similar(b)
+BackslashSolver(A)(x, b)
+@test sum(abs2, x - ones(10)) < 1e-12  # Should be more accurate than Pinv
+
+# Test Pinv (legacy solver, less efficient)
 @test sum(abs2, Pinv(A)(similar(b), b) - ones(10)) < 1e-6
+
+# Test that BackslashSolver handles multiple RHS
+B = hcat(b, 2*b)
+X = similar(B)
+BackslashSolver(A)(X, B) 
+@test sum(abs2, X[:, 1] - ones(10)) < 1e-12
+@test sum(abs2, X[:, 2] - 2*ones(10)) < 1e-12
+
+# Test with sparse matrices of different types
+for T in [Float32, Float64]
+    A_typed = T.(A)
+    b_typed = T.(b)
+    x_typed = similar(b_typed)
+    BackslashSolver(A_typed)(x_typed, b_typed)
+    @test sum(abs2, x_typed - ones(T, 10)) < 1e-6
+end
 end
 
 @testset "Multilevel" begin