diff --git a/src/Kaczmarz.jl b/src/Kaczmarz.jl
index 9303ca4..5f4a96f 100644
--- a/src/Kaczmarz.jl
+++ b/src/Kaczmarz.jl
@@ -181,9 +181,9 @@ function init!(solver::Kaczmarz, state::KaczmarzState{T, vecT}, b::vecT; x0 = 0)
   if λ_ != λ_prev
     # A must be unchanged, since we do not store the original SM
     _, solver.denom, solver.rowindex = initkaczmarz(solver.A, λ_)
-    solver.rowIndexCycle = collect(1:length(rowindex))
+    solver.rowIndexCycle = collect(1:length(solver.rowindex))
     if solver.randomized
-      solver.probabilities = T.(rowProbabilities(solver.A, rowindex))
+      solver.probabilities = T.(rowProbabilities(solver.A, solver.rowindex))
     end
   end
 
diff --git a/test/testKaczmarz.jl b/test/testKaczmarz.jl
index 18b4904..709f019 100644
--- a/test/testKaczmarz.jl
+++ b/test/testKaczmarz.jl
@@ -118,6 +118,15 @@ Random.seed!(12345)
           x_approx = Array(solve!(S, arrayType(b)))
           @test norm(x - x_approx) / norm(x) ≈ 0 atol = 0.1
         end
+
+        # Normalization
+        for strategy in [SystemMatrixBasedNormalization(), MeasurementBasedNormalization()]
+          l2 = L2Regularization(0.1)
+          S = createLinearSolver(solver, arrayType(A), iterations=200, randomized=true, reg = l2, normalizeReg = strategy)
+          x_approx = Array(solve!(S, arrayType(b)))
+          @test norm(x - x_approx) / norm(x) ≈ 0 atol = 0.1
+        end
+
       end
     end
   end