Explicitly pass rng to test random numbers

Author: Pawel Latawiec

test/bicgstabl.jl

@@ -7,12 +7,12 @@ using LinearAlgebra
 
 @testset ("BiCGStab(l)") begin
 
-Random.seed!(1234321)
+rng = Random.Xoshiro(12345)
 n = 20
 
 @testset "Matrix{$T}" for T in (Float32, Float64, ComplexF32, ComplexF64)
-    Random.seed!(123) # Issue #316 (test sensitive to the rng)
-    A = rand(T, n, n) + 15I
+    rng_temp = Random.Xoshiro(12345) # Issue #316 (test sensitive to the rng)
+    A = rand(rng_temp, T, n, n) + 15I
     x = ones(T, n)
     b = A * x
 
@@ -25,7 +25,7 @@ n = 20
         @test norm(A * x1 - b) / norm(b) ≤ reltol
 
         # With an initial guess
-        x_guess = rand(T, n)
+        x_guess = rand(rng, T, n)
         x2, his2 = bicgstabl!(x_guess, A, b, l, max_mv_products = 100, log = true, reltol = reltol)
         @test isa(his2, ConvergenceHistory)
         @test x2 == x_guess
@@ -37,7 +37,7 @@ n = 20
             continue
         end
         # Do an exact LU decomp of a nearby matrix
-        F = lu(A + rand(T, n, n))
+        F = lu(A + rand(rng, T, n, n))
         x3, his3 = bicgstabl(A, b, Pl = F, l, max_mv_products = 100, log = true, reltol = reltol)
         @test norm(A * x3 - b) / norm(b) ≤ reltol
     end

test/cg.jl

@@ -19,15 +19,15 @@ ldiv!(y, P::JacobiPrec, x) = y .= x ./ P.diagonal
 
 @testset "Conjugate Gradients" begin
 
-Random.seed!(1234321)
+rng = Random.Xoshiro(1234)
 
 @testset "Small full system" begin
     n = 10
 
     @testset "Matrix{$T}" for T in (Float32, Float64, ComplexF32, ComplexF64)
-        A = rand(T, n, n)
+        A = rand(rng, T, n, n)
         A = A' * A + I
-        b = rand(T, n)
+        b = rand(rng, T, n)
         reltol = √eps(real(T))
 
         x,ch = cg(A, b; reltol=reltol, maxiter=2n, log=true)

test/chebyshev.jl

@@ -5,8 +5,8 @@ using Test
 using Random
 using LinearAlgebra
 
-function randSPD(T, n)
-    A = rand(T, n, n) + n * I
+function randSPD(rng, T, n)
+    A = rand(rng, T, n, n) + n * I
     A' * A
 end
 
@@ -21,11 +21,11 @@ end
 @testset "Chebyshev" begin
 
 n = 10
-Random.seed!(1234321)
+rng = Random.Xoshiro(1234)
 
 @testset "Matrix{$T}" for T in (Float32, Float64, ComplexF32, ComplexF64)
-    A = randSPD(T, n)
-    b = rand(T, n)
+    A = randSPD(rng, T, n)
+    b = rand(rng, T, n)
     reltol = √(eps(real(T)))
     abstol = reltol
 
@@ -40,15 +40,15 @@ Random.seed!(1234321)
 
     @testset "With an initial guess" begin
         λ_min, λ_max = approx_eigenvalue_bounds(A)
-        x0 = rand(T, n)
+        x0 = rand(rng, T, n)
         initial_residual = norm(A * x0 - b)
         x, history = chebyshev!(x0, A, b, λ_min, λ_max, reltol=reltol, maxiter=10n, log=true)
         @test isa(history, ConvergenceHistory)
         @test history.isconverged
         @test x == x0
         @test norm(A * x - b) ≤ reltol * initial_residual
 
-        x0 = rand(T, n)
+        x0 = rand(rng, T, n)
         x, history = chebyshev!(x0, A, b, λ_min, λ_max, abstol=abstol, reltol=zero(real(T)), maxiter=10n, log=true)
         @test isa(history, ConvergenceHistory)
         @test history.isconverged
@@ -57,7 +57,7 @@ Random.seed!(1234321)
     end
 
     @testset "With a preconditioner" begin
-        B = randSPD(T, n)
+        B = randSPD(rng, T, n)
         B_fact = cholesky!(B, Val(false))
         λ_min, λ_max = approx_eigenvalue_bounds(B_fact \ A)
         x, history = chebyshev(A, b, λ_min, λ_max, Pl = B_fact, reltol=reltol, maxiter=10n, log=true)

test/common.jl

@@ -6,7 +6,7 @@ using IterativeSolvers
 using Test
 using Random
 
-Random.seed!(1234321)
+rng = Random.Xoshiro(1234)
 
 @testset "Basic operations" begin
 

test/gmres.jl

@@ -10,12 +10,12 @@ using SparseArrays
 #GMRES
 @testset "GMRES" begin
 
-Random.seed!(1234321)
+rng = Random.Xoshiro(1234)
 n = 10
 
 @testset "Matrix{$T}" for T in (Float32, Float64, ComplexF32, ComplexF64)
-    A = rand(T, n, n) + I
-    b = rand(T, n)
+    A = rand(rng, T, n, n) + I
+    b = rand(rng, T, n)
     F = lu(A)
     reltol = √eps(real(T))
 
@@ -36,8 +36,8 @@ n = 10
 end
 
 @testset "SparseMatrixCSC{$T, $Ti}" for T in (Float64, ComplexF64), Ti in (Int64, Int32)
-    A = sprand(T, n, n, 0.5) + I
-    b = rand(T, n)
+    A = sprand(rng, T, n, n, 0.5) + I
+    b = rand(rng, T, n)
     F = lu(A)
     reltol = √eps(real(T))
 
@@ -58,7 +58,7 @@ end
 
 @testset "Linear operator defined as a function" begin
     A = LinearMap(cumsum!, 100; ismutating=true)
-    b = rand(100)
+    b = rand(rng, 100)
     reltol = 1e-5
 
     x = gmres(A, b; reltol=reltol, maxiter=2000)

test/idrs.jl

@@ -10,11 +10,11 @@ using SparseArrays
 
 n = 10
 m = 6
-Random.seed!(1234567)
+rng = Random.Xoshiro(12345)
 
 @testset "Matrix{$T}" for T in (Float32, Float64, ComplexF32, ComplexF64)
-    A = rand(T, n, n) + n * I
-    b = rand(T, n)
+    A = rand(rng, T, n, n) + n * I
+    b = rand(rng, T, n)
     reltol = √eps(real(T))
 
     @testset "Without residual smoothing" begin
@@ -33,8 +33,8 @@ Random.seed!(1234567)
 end
 
 @testset "SparseMatrixCSC{$T, $Ti}" for T in (Float64, ComplexF64), Ti in (Int64, Int32)
-    A = sprand(T, n, n, 0.5) + n * I
-    b = rand(T, n)
+    A = sprand(rng, T, n, n, 0.5) + n * I
+    b = rand(rng, T, n)
     reltol = √eps(real(T))
 
     x, history = idrs(A, b, log=true)
@@ -43,8 +43,8 @@ end
 end
 
 @testset "SparseMatrixCSC{$T, $Ti} with preconditioner" for T in (Float64, ComplexF64), Ti in (Int64, Int32)
-    A = sprand(T, 1000, 1000, 0.1) + 30 * I
-    b = rand(T, 1000)
+    A = sprand(rng, T, 1000, 1000, 0.1) + 30 * I
+    b = rand(rng, T, 1000)
     reltol = √eps(real(T))
 
     x, history = idrs(A, b, log=true)

test/lal.jl

@@ -4,6 +4,10 @@ using SparseArrays
 using Test
 import Random
 
+@testset "LAL" begin
+
+rng = Random.Xoshiro(1234)
+
 # Equation references and identities from:
 # Freund, R. W., & Nachtigal, N. M. (1994). An Implementation of the QMR Method Based on Coupled Two-Term Recurrences. SIAM Journal on Scientific Computing, 15(2), 313–337. https://doi.org/10.1137/0915022
 
@@ -238,4 +242,5 @@ end
 
         test_lal_identities(ld_results)
     end 
+end
 end

test/limited_memory_matrices.jl

@@ -3,6 +3,8 @@ using LinearAlgebra
 using SparseArrays
 using Test
 
+@testset "Limited Memory Matrices" begin
+
 @testset "LimitedMemoryMatrix" begin
     A = IS.LimitedMemoryMatrix{Float64, Matrix{Float64}}(fill(1.0, 4, 4), 4, 4)
     @test A[1, 1] == 1
@@ -67,4 +69,6 @@ end
         0.0 0.0 3.0 4.0
         0.0 0.0 0.0 4.0
     ]
+end
+
 end