Merge pull request #71 from mohamed82008/mt/iterativesolvers9

Update to support IterativeSolvers.jl 0.9 API

Author: ranjanan

Project.toml

@@ -1,6 +1,6 @@
 name = "AlgebraicMultigrid"
 uuid = "2169fc97-5a83-5252-b627-83903c6c433c"
-version = "0.3.0"
+version = "0.4.0"
 
 [deps]
 CompatHelper = "aa819f21-2bde-4658-8897-bab36330d9b7"
@@ -14,7 +14,7 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 CompatHelper = "1.3.0"
-IterativeSolvers = "0.8.3"
+IterativeSolvers = "0.9"
 julia = "1.4.0"
 
 [extras]

src/multilevel.jl

@@ -132,7 +132,8 @@ Arguments
 
 Keyword Arguments
 =================
-* tol::Float64 - tolerance criteria for convergence
+* reltol::Float64 - relative tolerance criteria for convergence, the absolute tolerance will be `reltol * norm(b)`
+* abstol::Float64 - absolute tolerance criteria for convergence
 * maxiter::Int64 - maximum number of iterations to execute
 * verbose::Bool - display residual at each iteration
 * log::Bool - return vector of residuals along with solution
@@ -147,24 +148,24 @@ end
 function solve!(x, ml::MultiLevel, b::AbstractArray{T},
                                     cycle::Cycle = V();
                                     maxiter::Int = 100,
-                                    tol::Float64 = 1e-5,
+                                    abstol::Real = zero(real(eltype(b))),
+                                    reltol::Real = sqrt(eps(real(eltype(b)))),
                                     verbose::Bool = false,
                                     log::Bool = false,
                                     calculate_residual = true) where {T}
 
     A = length(ml) == 1 ? ml.final_A : ml.levels[1].A
     V = promote_type(eltype(A), eltype(b))
-    tol = eltype(b)(tol)
     log && (residuals = Vector{V}())
     normres = normb = norm(b)
     if normb != 0
-        tol *= normb
+        abstol = max(reltol * normb, abstol)
     end
     log && push!(residuals, normb)
 
     res = ml.workspace.res_vecs[1]
     itr = lvl = 1
-    while itr <= maxiter && (!calculate_residual || normres > tol)
+    while itr <= maxiter && (!calculate_residual || normres > abstol)
         if length(ml) == 1
             ml.coarse_solver(x, b)
         else

test/runtests.jl

@@ -151,7 +151,7 @@ p = aspreconditioner(ml)
 b = zeros(n)
 b[1] = 1
 b[2] = -1
-x = solve(p.ml, A * ones(n), maxiter = 1, tol = 1e-12)
+x = solve(p.ml, A * ones(n), maxiter = 1, abstol = 1e-12)
 diff = x - [  1.88664780e-16,   2.34982727e-16,   2.33917697e-16,
          8.77869044e-17,   7.16783490e-17,   1.43415460e-16,
          3.69199021e-17,   9.70950385e-17,   4.77034895e-17,
@@ -169,7 +169,7 @@ diff = x - [  1.88664780e-16,   2.34982727e-16,   2.33917697e-16,
         -6.76965535e-16,  -7.00643227e-16,  -6.23581397e-16,
         -7.03016682e-16]
 @test sum(abs2, diff) < 1e-8
-x = solve(p.ml, b, maxiter = 1, tol = 1e-12)
+x = solve(p.ml, b, maxiter = 1, abstol = 1e-12)
 diff = x - [ 0.76347046, -0.5498286 , -0.2705487 , -0.15047352, -0.10248021,
         0.60292674, -0.11497073, -0.08460548, -0.06931461,  0.38230708,
        -0.055664  , -0.04854558, -0.04577031,  0.09964325,  0.01825624,
@@ -199,7 +199,7 @@ diff = x - [ 0.82365077, -0.537589  , -0.30632349, -0.19370186, -0.14773294,
 ml = ruge_stuben(A)
 p = aspreconditioner(ml)
 
-x = cg(A, b, Pl = p, maxiter = 100_000, tol = 1e-6)
+x = cg(A, b, Pl = p, maxiter = 100_000, reltol = 1e-6)
 diff = x - [0.823762, -0.537478, -0.306212, -0.19359, -0.147621, 0.685002,
             -0.155389, -0.127703, -0.111867, 0.453735, -0.0856607, -0.0858715,
             -0.0846678, 0.129962, 0.0281662, -0.0389642, -0.0593981, -0.0653311,
@@ -210,7 +210,7 @@ diff = x - [0.823762, -0.537478, -0.306212, -0.19359, -0.147621, 0.685002,
             0.0511691, 0.0502043, 0.0498349, 0.0498134]
 @test sum(abs2, diff) < 1e-8
 
-x = solve(ml, b, maxiter = 1, tol = 1e-12)
+x = solve(ml, b, maxiter = 1, reltol = 1e-12)
 diff = x - [0.775725, -0.571202, -0.290989, -0.157001, -0.106981, 0.622652,
             -0.122318, -0.0891874, -0.0709834, 0.392621, -0.055544, -0.0507485,
             -0.0466376, 0.107175, 0.0267468, -0.0200843, -0.0282827, -0.0299929,