diff --git a/examples/low_dim_in_high_dim.jl b/examples/low_dim_in_high_dim.jl
index 33a7a0402..1f9dd534f 100644
--- a/examples/low_dim_in_high_dim.jl
+++ b/examples/low_dim_in_high_dim.jl
@@ -9,6 +9,8 @@ using Distributions
 import MathOptInterface
 const MOI = MathOptInterface
 
+include("cube_blmo.jl")
+
 # The example from  "Optimizing a low-dimensional convex function over a high-dimensional cube"
 # by Christoph Hunkenschröder, Sebastian Pokutta, Robert Weismantel
 # https://arxiv.org/abs/2204.05266. 
diff --git a/examples/nonlinear.jl b/examples/nonlinear.jl
index e426b7a5b..4aa36a56c 100644
--- a/examples/nonlinear.jl
+++ b/examples/nonlinear.jl
@@ -1,4 +1,3 @@
-using SCIP
 using FrankWolfe
 using LinearAlgebra
 import MathOptInterface
@@ -8,23 +7,41 @@ import Bonobo
 using Printf
 using Dates
 
-const MOI = MathOptInterface
+# using SCIP
+# const MOI = MathOptInterface
 
-n = 5
+include("cube_blmo.jl")
+
+n = 40
 seed = 10
 
 Random.seed!(seed)
 
-o = SCIP.Optimizer()
-MOI.set(o, MOI.Silent(), true)
-MOI.empty!(o)
-x = MOI.add_variables(o, n)
-for xi in x
-    MOI.add_constraint(o, xi, MOI.ZeroOne())
-    MOI.add_constraint(o, xi, MOI.GreaterThan(0.0))
-    MOI.add_constraint(o, xi, MOI.LessThan(1.0))
+################################################################
+# alternative implementation of LMO using MOI and SCIP
+################################################################
+# o = SCIP.Optimizer()
+# MOI.set(o, MOI.Silent(), true)
+# MOI.empty!(o)
+# x = MOI.add_variables(o, n)
+# for xi in x
+#     MOI.add_constraint(o, xi, MOI.ZeroOne())
+#     MOI.add_constraint(o, xi, MOI.GreaterThan(0.0))
+#     MOI.add_constraint(o, xi, MOI.LessThan(1.0))
+# end
+# lmo = FrankWolfe.MathOptLMO(o)
+
+
+################################################################
+# LMO via CubeBLMO
+################################################################
+int_vars = collect(1:n)
+bounds = Boscia.IntegerBounds()
+for i in 1:n
+    push!(bounds, (i, 0.0), :greaterthan)
+    push!(bounds, (i, 1.0), :lessthan)
 end
-lmo = FrankWolfe.MathOptLMO(o)
+lmo = CubeBLMO(n, int_vars, bounds)
 
 const A = let
     A = randn(n, n)
@@ -47,6 +64,6 @@ function grad!(storage, x)
     return mul!(storage, A, y, -2, 2)
 end
 
-x, _, _ = Boscia.solve(f, grad!, lmo, verbose=true, print_iter=1)
+x, _, _ = Boscia.solve(f, grad!, lmo, verbose=true, print_iter=500)
 
 @show x