Single precision on smoothed aggregation seems to work

Author: ranjanan

src/aggregate.jl

@@ -95,7 +95,7 @@ function aggregation(::StandardAggregation, S)
 
     Tp = collect(1:n+1)
     x .= x .+ 1
-    Tx = ones(length(x))
+    Tx = ones(eltype(S), length(x))
 
     SparseMatrixCSC(N, M, Tp, x, Tx)
 end

src/aggregation.jl

@@ -1,4 +1,4 @@
-function smoothed_aggregation{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti},
+function smoothed_aggregation(A::SparseMatrixCSC{T,V},
                         symmetry = HermitianSymmetry(),
                         strength = SymmetricStrength(),
                         aggregate = StandardAggregation(),
@@ -10,12 +10,12 @@ function smoothed_aggregation{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti},
                         max_coarse = 10,
                         diagonal_dominance = false,
                         keep = false,
-                        coarse_solver = Pinv())
+                        coarse_solver = Pinv()) where {T,V}
 
 
     n = size(A, 1)
     # B = kron(ones(n, 1), eye(1))
-    B = ones(n)
+    B = ones(T,n)
 
     #=max_levels, max_coarse, strength =
         levelize_strength_or_aggregation(max_levels, max_coarse, strength)
@@ -28,7 +28,7 @@ function smoothed_aggregation{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti},
     # agg = [aggregate for _ in 1:max_levels - 1]
     # sm = [smooth for _ in 1:max_levels]
 
-    levels = Vector{Level{Tv,Ti}}()
+    levels = Vector{Level{T,V}}()
     bsr_flag = false
 
     while length(levels) + 1 < max_levels && size(A, 1) > max_coarse

src/multilevel.jl

@@ -1,7 +1,7 @@
-struct Level{Ti,Tv}
-    A::SparseMatrixCSC{Ti,Tv}
-    P::SparseMatrixCSC{Ti,Tv}
-    R::SparseMatrixCSC{Ti,Tv}
+struct Level{T,V}
+    A::SparseMatrixCSC{T,V}
+    P::SparseMatrixCSC{T,V}
+    R::SparseMatrixCSC{T,V}
 end
 
 struct MultiLevel{S, Pre, Post, Ti, Tv}

src/smoother.jl

@@ -110,7 +110,7 @@ end
 function weight(::DiagonalWeighting, S, ω)
     D_inv = 1 ./ diag(S)
     D_inv_S = scale_rows(S, D_inv)
-    (ω / approximate_spectral_radius(D_inv_S)) * D_inv_S
+    (eltype(S)(ω) / approximate_spectral_radius(D_inv_S)) * D_inv_S
     #(ω) * D_inv_S
 end
 

src/strength.jl

@@ -77,13 +77,11 @@ SymmetricStrength() = SymmetricStrength(0.)
 
 function strength_of_connection{T}(s::SymmetricStrength{T}, A, bsr_flag = false)
 
-
-
     θ = s.θ
 
     if bsr_flag && θ == 0
         S = SparseMatrixCSC(size(A)...,
-                    A.colptr, A.rowval, ones(size(A.rowval)))
+                    A.colptr, A.rowval, ones(eltype(A), size(A.rowval)))
         return S
     else
         S = deepcopy(A)

src/utils.jl

@@ -5,11 +5,11 @@ function approximate_spectral_radius(A, tol = 0.01,
     symmetric = false
 
     # Initial guess
-    v0 = rand(size(A,1))
+    v0 = rand(eltype(A), size(A,1))
     maxiter = min(size(A, 1), maxiter)
     ev = zeros(eltype(A), maxiter)
     max_index = 0
-    X = zeros(size(A,1), maxiter)
+    X = zeros(eltype(A), size(A,1), maxiter)
 
     for i in 1:restart+1
         evect, ev, H, V, flag =
@@ -90,8 +90,7 @@ function approximate_eigenvalues(A, tol, maxiter, symmetric, v0)
         scale!(w, 1/H[j+1,j])
         push!(V, w)
     end
-
-    Eigs, Vects = eig(H[1:maxiter, 1:maxiter], eye(maxiter))
+    Eigs, Vects = eig(H[1:maxiter, 1:maxiter], eye(eltype(A), maxiter))
 
     Vects, Eigs, H, V, flag
 end