updated examples to use cubesimpleblmo

ZIB-IOL · Nov 6, 2023 · ee5cc6c · ee5cc6c
1 parent 92825dc
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Showing 3 changed files with 38 additions and 30 deletions.
diff --git a/examples/low_dim_in_high_dim.jl b/examples/low_dim_in_high_dim.jl
@@ -9,8 +9,6 @@ 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. 
@@ -53,17 +51,17 @@ end
     @test f(x) <= f(result[:raw_solution]) + 1e-6
 end
 
-@testset "Low-dimensional function (CubeBLMO)" begin
+@testset "Low-dimensional function (CubeSimpleBLMO)" begin
 
     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
-    blmo = CubeBLMO(n, int_vars, bounds)
 
-    x, _, result = Boscia.solve(f, grad!, blmo, verbose=true)
+    lbs = zeros(n)
+    ubs = ones(n)
+
+    sblmo = Boscia.CubeSimpleBLMO(lbs, ubs, int_vars)
+
+    # modified solve call from managed_blmo.jl automatically wraps sblmo into a managed_blmo
+    x, _, result = Boscia.solve(f, grad!, sblmo, lbs[int_vars], ubs[int_vars], int_vars, n, verbose=true)
 
     if n < 15  # only do for small n 
         valopt, xopt = Boscia.min_via_enum(f, n)

diff --git a/examples/nonlinear.jl b/examples/nonlinear.jl
@@ -10,8 +10,6 @@ using Dates
 # using SCIP
 # const MOI = MathOptInterface
 
-include("cube_blmo.jl")
-
 n = 40
 seed = 10
 
@@ -33,15 +31,16 @@ Random.seed!(seed)
 
 
 ################################################################
-# LMO via CubeBLMO
+# LMO via CubeSimpleBLMO
 ################################################################
 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 = CubeBLMO(n, int_vars, bounds)
+
+lbs = zeros(n)
+ubs = ones(n)
+
+sblmo = Boscia.CubeSimpleBLMO(lbs, ubs, int_vars)
+# wrap the sblmo into a bound manager
+lmo = Boscia.ManagedBoundedLMO(sblmo, lbs[int_vars], ubs[int_vars], n, int_vars)
 
 const A = let
     A = randn(n, n)

diff --git a/examples/worst-case.jl b/examples/worst-case.jl
@@ -2,7 +2,7 @@ using Boscia
 using FrankWolfe
 using Test
 using Random
-using SCIP
+# using SCIP
 using LinearAlgebra
 using Distributions
 import MathOptInterface
@@ -33,16 +33,27 @@ n = 10
 
 @testset "Interface - 2-norm over hypercube -- α = $alpha" for alpha in (0.0, 0.05)
     diff_point = 0.5 * ones(n) + Random.rand(n) * alpha * 1 / n
-    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.GreaterThan(0.0))
-        MOI.add_constraint(o, xi, MOI.LessThan(1.0))
-        MOI.add_constraint(o, xi, MOI.ZeroOne())
-    end
-    lmo = FrankWolfe.MathOptLMO(o)
+
+    # SCIP variant of the LMO
+    # 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.GreaterThan(0.0))
+    #     MOI.add_constraint(o, xi, MOI.LessThan(1.0))
+    #     MOI.add_constraint(o, xi, MOI.ZeroOne())
+    # end
+    # lmo = FrankWolfe.MathOptLMO(o)
+
+    int_vars = collect(1:n)
+
+    lbs = zeros(n)
+    ubs = ones(n)
+
+    sblmo = Boscia.CubeSimpleBLMO(lbs, ubs, int_vars)
+    # wrap the sblmo into a bound manager
+    lmo = Boscia.ManagedBoundedLMO(sblmo, lbs[int_vars], ubs[int_vars], n, int_vars)
 
     function f(x)
         return 0.5 * sum((x .- diff_point) .^ 2)