Fix in the lowerbound tightening (#204)

Sharpness cannot be used out of the box for the Optimal Design problems.

examples/birkhoff.jl

@@ -76,12 +76,12 @@ function build_birkhoff_lmo()
         # doubly stochastic constraints
         MOI.add_constraint.(
             o,
-            vec(sum(X[i], dims=1, init=MOI.ScalarAffineFunction{Float64}([], 0.0))),
+            X[i] * ones(n),
             MOI.EqualTo(1.0),
         )
         MOI.add_constraint.(
             o,
-            vec(sum(X[i], dims=2, init=MOI.ScalarAffineFunction{Float64}([], 0.0))),
+            X[i]' * ones(n),
             MOI.EqualTo(1.0),
         )
         # 0 ≤ Y_i ≤ X_i

examples/optimal_experiment_design.jl

@@ -40,88 +40,114 @@ include("oed_utils.jl")
     https://github.com/ZIB-IOL/FrankWolfe.jl/blob/master/examples/optimal_experiment_design.jl
 """
 
-m = 40
+
+m = 50
 verbose = true
 
 ## A-Optimal Design Problem
-
 @testset "A-Optimal Design" begin
 
     Ex_mat, n, N, ub = build_data(m)
 
-    # sharpness constants
-    σ = minimum(Ex_mat' * Ex_mat)
-    λ_max = maximum(ub) * maximum([norm(Ex_mat[i,:])^2 for i=1:size(Ex_mat,1)])
-    θ = 1/2
-    M = sqrt(λ_max^3/  n * σ^4)
-
-
-    g, grad! = build_a_criterion(Ex_mat, build_safe=true)
+    g, grad! = build_a_criterion(Ex_mat, build_safe=false)
     blmo = build_blmo(m, N, ub)
-    x0, active_set = build_start_point(Ex_mat, N, ub)
-    z = greedy_incumbent(Ex_mat, N, ub)
-    domain_oracle = build_domain_oracle(Ex_mat, n)
-    line_search = FrankWolfe.MonotonicGenericStepsize(FrankWolfe.Adaptive(), domain_oracle)
     heu = Boscia.Heuristic(Boscia.rounding_hyperplane_heuristic, 0.7, :hyperplane_aware_rounding)
+    domain_oracle = build_domain_oracle(Ex_mat, n)
 
-    x, _, result = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, time_limit=10, verbose=false, domain_oracle=domain_oracle, custom_heuristics=[heu], sharpness_exponent=θ, sharpness_constant=M, line_search=line_search)
-
-    _, active_set = build_start_point(Ex_mat, N, ub)
+    # precompile
+    line_search = FrankWolfe.MonotonicGenericStepsize(FrankWolfe.Adaptive(), domain_oracle)
+    x0, active_set = build_start_point(Ex_mat, N, ub)
     z = greedy_incumbent(Ex_mat, N, ub)
-    x, _, result = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, verbose=verbose, domain_oracle=domain_oracle, custom_heuristics=[heu], sharpness_exponent=θ, sharpness_constant=M, line_search=line_search) #sharpness_exponent=θ, sharpness_constant=M,
-
-    gradient = similar(x)
-    grad!(gradient, x)
-    v = Boscia.compute_extreme_point(blmo, gradient)
-    dual_gap = FrankWolfe.dot(gradient, x) - FrankWolfe.dot(gradient, v)
-    @show dual_gap
+    x, _, _ = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, time_limit=10, verbose=false, domain_oracle=domain_oracle, custom_heuristics=[heu], line_search=line_search)
 
-    blmo = build_blmo(m, N, ub)
-    _, active_set = build_start_point(Ex_mat, N, ub)
+    # proper run with MGLS and Adaptive
+    line_search = FrankWolfe.MonotonicGenericStepsize(FrankWolfe.Adaptive(), domain_oracle)
+    x0, active_set = build_start_point(Ex_mat, N, ub)
     z = greedy_incumbent(Ex_mat, N, ub)
-    domain_oracle = build_domain_oracle(Ex_mat, n)
-    heu = Boscia.Heuristic(Boscia.rounding_hyperplane_heuristic, 0.7, :hyperplane_aware_rounding)
-
-    x_s, _, result = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, verbose=verbose, line_search=FrankWolfe.Secant(domain_oracle=domain_oracle), domain_oracle=domain_oracle, sharpness_exponent=θ, sharpness_constant=M, custom_heuristics=[heu]) #sharpness_exponent=θ, sharpness_constant=M, 
-
-    @show x
-    @show x_s
-    @show g(x), g(x_s)
-    @test isapprox(g(x), g(x_s), atol=1e-3, rtol=5e-2)
+    x, _, result = Boscia.solve(
+        g, 
+        grad!, 
+        blmo, 
+        active_set=active_set, 
+        start_solution=z, 
+        verbose=verbose, 
+        domain_oracle=domain_oracle, 
+        custom_heuristics=[heu], 
+        line_search=line_search,
+    )
+
+    # Run with Secant    
+    x0, active_set = build_start_point(Ex_mat, N, ub)
+    z = greedy_incumbent(Ex_mat, N, ub)
+    line_search = FrankWolfe.Secant(domain_oracle=domain_oracle)
+
+    x_s, _, result_s = Boscia.solve(
+        g, 
+        grad!, 
+        blmo, 
+        active_set=active_set, 
+        start_solution=z, 
+        verbose=verbose, 
+        domain_oracle=domain_oracle, 
+        custom_heuristics=[heu], 
+        line_search=line_search,
+    )
+
+    @test result_s[:dual_bound] <= g(x) + 1e-4
+    @test result[:dual_bound] <= g(x_s) + 1e-4
+    @test isapprox(g(x), g(x_s), atol=1e-3) 
 end 
 
 ## D-Optimal Design Problem
-
 @testset "D-optimal Design" begin
     Ex_mat, n, N, ub = build_data(m)
 
-    # sharpness constants
-    σ = minimum(Ex_mat' * Ex_mat)
-    λ_max = maximum(ub) * maximum([norm(Ex_mat[i,:])^2 for i=1:size(Ex_mat,1)])
-    θ = 1/2
-    M = sqrt(2 * λ_max^2/ n * σ^4 )
-
-
-    g, grad! = build_d_criterion(Ex_mat, build_safe=true)
+    g, grad! = build_d_criterion(Ex_mat, build_safe=false)
     blmo = build_blmo(m, N, ub)
-    x0, active_set = build_start_point(Ex_mat, N, ub)
-    z = greedy_incumbent(Ex_mat, N, ub)
-    domain_oracle = build_domain_oracle(Ex_mat, n)
     heu = Boscia.Heuristic(Boscia.rounding_hyperplane_heuristic, 0.7, :hyperplane_aware_rounding)
+    domain_oracle = build_domain_oracle(Ex_mat, n)
 
-    x, _, result = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, verbose=verbose,  domain_oracle=domain_oracle, sharpness_exponent=θ, sharpness_constant=M, custom_heuristics=[heu]) 
-
-    blmo = build_blmo(m, N, ub)
+    # precompile
+    line_search = FrankWolfe.MonotonicGenericStepsize(FrankWolfe.Adaptive(), domain_oracle)
     x0, active_set = build_start_point(Ex_mat, N, ub)
     z = greedy_incumbent(Ex_mat, N, ub)
-    domain_oracle = build_domain_oracle(Ex_mat, n)
-    heu = Boscia.Heuristic(Boscia.rounding_hyperplane_heuristic, 0.7, :hyperplane_aware_rounding)
+    x, _, _ = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, time_limit=10, verbose=false, domain_oracle=domain_oracle, custom_heuristics=[heu], line_search=line_search)
 
-    x_s, _, result = Boscia.solve(g, grad!, blmo, active_set=active_set, start_solution=z, verbose=verbose, line_search=FrankWolfe.Secant(domain_oracle=domain_oracle), domain_oracle=domain_oracle, sharpness_exponent=θ, sharpness_constant=M, custom_heuristics=[heu]) 
-
-    @show x 
-    @show x_s
-    @show g(x), g(x_s)
-    @test isapprox(g(x), g(x_s), atol=1e-4, rtol=1e-2)
+    # proper run with MGLS and Adaptive
+    line_search = FrankWolfe.MonotonicGenericStepsize(FrankWolfe.Adaptive(), domain_oracle)
+    x0, active_set = build_start_point(Ex_mat, N, ub)
+    z = greedy_incumbent(Ex_mat, N, ub)
+    x, _, result = Boscia.solve(
+        g, 
+        grad!, 
+        blmo, 
+        active_set=active_set, 
+        start_solution=z, 
+        verbose=verbose, 
+        domain_oracle=domain_oracle, 
+        custom_heuristics=[heu], 
+        line_search=line_search,
+    )
+
+    # Run with Secant    
+    x0, active_set = build_start_point(Ex_mat, N, ub)
+    z = greedy_incumbent(Ex_mat, N, ub)
+    line_search = FrankWolfe.Secant(domain_oracle=domain_oracle)
+
+    x_s, _, result_s = Boscia.solve(
+        g, 
+        grad!, 
+        blmo, 
+        active_set=active_set, 
+        start_solution=z, 
+        verbose=verbose, 
+        domain_oracle=domain_oracle, 
+        custom_heuristics=[heu], 
+        line_search=line_search,
+    )
+
+    @test result_s[:dual_bound] <= g(x)
+    @test result[:dual_bound] <= g(x_s)
+    @test isapprox(g(x), g(x_s), rtol=1e-2) 
 end
 

src/node.jl

@@ -283,7 +283,7 @@ function Bonobo.evaluate_node!(tree::Bonobo.BnBTree, node::FrankWolfeNode)
     elseif node.id == 1
         @debug "Lower bound of root node: $(lower_bound)"
         @debug "Current incumbent: $(tree.incumbent)"
-        @assert lower_bound <= tree.incumbent + node.fw_dual_gap_limit "lower_bound <= tree.incumbent + node.fw_dual_gap_limit : $(lower_bound) <= $(tree.incumbent + node.fw_dual_gap_limit)"
+        @assert lower_bound <= tree.incumbent + dual_gap "lower_bound <= tree.incumbent + dual_gap : $(lower_bound) <= $(tree.incumbent + dual_gap)"
     end
 
     # Call heuristic 

src/tightenings.jl

@@ -218,7 +218,13 @@ function tightening_lowerbound(tree, node, x, lower_bound)
             @debug "Using sharpness θ=$θ and M=$M"
             fx = tree.root.problem.f(x)
 
+            if node.dual_gap < 0.0
+                @assert abs(node.dual_gap) > eps() "node dual gap is negative: $(node.dual_gap)"
+                node.dual_gap = 0.0
+            end
+
             sharpness_bound = M^(- 1 / θ) * 1/ 2 * (sqrt(bound_improvement) - M / 2 * node.dual_gap^θ)^(1 / θ) + fx - node.dual_gap
+
             @debug "Sharpness: $lower_bound -> $sharpness_bound"
             @assert num_fractional == 0 || sharpness_bound >= lower_bound "$(num_fractional) == 0 || $(sharpness_bound) > $(lower_bound)"
         end
@@ -283,10 +289,6 @@ function prune_children(tree, node, lower_bound_base, x, vidx)
                 @debug "prune right, from $(node.lb) -> $new_bound_right, ub $(tree.incumbent), lb $(node.lb)"
                 prune_right = true
             end
-            @assert !(
-                (new_bound_left > tree.incumbent + tree.root.options[:dual_gap]) &&
-                (new_bound_right > tree.incumbent + tree.root.options[:dual_gap])
-            ) 
         end
     end
 

src/utilities.jl

@@ -229,6 +229,42 @@ function sparse_min_via_enum(f, n, k, values=fill(0:1, n))
     return best_val, best_sol
 end
 
+function min_via_enum_simplex(f, n, N, values=fill(0:1,n))
+    solutions = Iterators.product(values...)
+    best_val = Inf
+    best_sol = nothing
+    for sol in solutions
+        sol_vec = collect(sol)
+        if sum(sol_vec) > N 
+            continue
+        end
+        val = f(sol_vec)
+        if best_val > val
+            best_val = val
+            best_sol = sol_vec
+        end
+    end
+    return best_val, best_sol
+end
+
+function min_via_enum_prob_simplex(f, n, N, values=fill(0:1,n))
+    solutions = Iterators.product(values...)
+    best_val = Inf
+    best_sol = nothing
+    for sol in solutions
+        sol_vec = collect(sol)
+        if sum(sol_vec) != N 
+            continue
+        end
+        val = f(sol_vec)
+        if best_val > val
+            best_val = val
+            best_sol = sol_vec
+        end
+    end
+    return best_val, best_sol
+end
+
 
 # utility function to print the values of the parameters
 _value_to_print(::Bonobo.BestFirstSearch) = "Move best bound"