fix AMG with matrix input (#41)

* fix AMG with matrix input

* bump version

Project.toml

@@ -1,6 +1,6 @@
 name = "Preconditioners"
 uuid = "af69fa37-3177-5a40-98ee-561f696e4fcd"
-version = "0.6"
+version = "0.6.1"
 
 [deps]
 AlgebraicMultigrid = "2169fc97-5a83-5252-b627-83903c6c433c"

src/amg.jl

@@ -25,13 +25,19 @@ AMGPreconditioner(A::AbstractMatrix; kwargs...) = AMGPreconditioner(RugeStuben,
 AMGPreconditioner{T}(A::AbstractMatrix; kwargs...) where T = AMGPreconditioner(T, A; kwargs...)
 
 @inline function \(p::AMGPreconditioner, b)
-    x = copy(b); 
+    x = copy(b)
     return ldiv!(x, AMG.Preconditioner(p.ml, p.cycle), b)
 end
 @inline *(p::AMGPreconditioner, b) = AMG.Preconditioner(p.ml, p.cycle) * b
 @inline ldiv!(p::AMGPreconditioner, b) = b .= p \ b
-@inline function ldiv!(x, p::AMGPreconditioner, b)
+@inline function ldiv!(x::AbstractVector, p::AMGPreconditioner, b::AbstractVector)
     x .= b
     return ldiv!(x, AMG.Preconditioner(p.ml, p.cycle), b)
 end
+@inline function ldiv!(x::AbstractMatrix, p::AMGPreconditioner, b::AbstractMatrix)
+    foreach(zip(eachcol(x), eachcol(b))) do (_x, _b)
+        ldiv!(_x, p, _b)
+    end
+    return x
+end
 @inline mul!(b, p::AMGPreconditioner, x) = mul!(b, AMG.Preconditioner(p.ml, p.cycle), x)

test/runtests.jl

@@ -1,9 +1,9 @@
-using LinearAlgebra: I, diag, ldiv!, norm, Symmetric, Hermitian
+using LinearAlgebra: I, diag, ldiv!, norm, Symmetric, Hermitian, eigen
 using SparseArrays: sprand
 using Preconditioners: CholeskyPreconditioner, DiagonalPreconditioner
 using Preconditioners: RugeStuben, SmoothedAggregation
 using Preconditioners: UpdatePreconditioner!, AMGPreconditioner
-using IterativeSolvers: cg
+using IterativeSolvers: cg, lobpcg
 using Random: seed!
 using Test: @test, @testset, @test_throws
 using OffsetArrays: OffsetMatrix, OffsetVector
@@ -68,3 +68,17 @@ end
     test_matrix(Symmetric(A), F, atol)
     test_matrix(Hermitian(A), F, atol)
 end
+
+@testset "AMG + LOBPCG" begin
+    A = sprand(1000, 1000, 0.01)
+    A = A + A' + 30I
+    X0 = randn(1000,1)
+    tol = 1e-3
+    F = lobpcg(A, false, X0, tol=tol, maxiter=200, P=AMGPreconditioner{SmoothedAggregation}(A))
+    @test eigen(Matrix(A)).values[1] ≈ F.λ[1] atol = 1e-3
+
+    X0 = randn(1000,2)
+    tol = 1e-3
+    F = lobpcg(A, false, X0, tol=tol, maxiter=200, P=AMGPreconditioner{SmoothedAggregation}(A))
+    @test eigen(Matrix(A)).values[1:2] ≈ F.λ atol = 1e-3
+end