format (#153)

ZIB-IOL · Oct 30, 2023 · c42b0d3 · c42b0d3
1 parent b028993
commit c42b0d3
Show file tree

Hide file tree

Showing 26 changed files with 400 additions and 300 deletions.
diff --git a/examples/HiGHS_example.jl b/examples/HiGHS_example.jl
@@ -24,11 +24,11 @@ end
 lmo = FrankWolfe.MathOptLMO(o)
 
 function f(x)
-    return 0.5 * sum((x.-diffw).^2)
+    return 0.5 * sum((x .- diffw) .^ 2)
 end
 
 function grad!(storage, x)
-    @. storage = x-diffw
+    @. storage = x - diffw
 end
 
-x, _, result = Boscia.solve(f, grad!, lmo, verbose = true)
+x, _, result = Boscia.solve(f, grad!, lmo, verbose=true)
diff --git a/examples/approx_planted_point.jl b/examples/approx_planted_point.jl
@@ -41,15 +41,15 @@ diffi = Random.rand(Bool, n) * 0.6 .+ 0.3
 
     @testset "Using Cube LMO" begin
         int_vars = collect(1:n)
-        
+
         bounds = Boscia.IntegerBounds()
-        for i in 1:n 
+        for i in 1:n
             push!(bounds, (i, 0.0), :greaterthan)
             push!(bounds, (i, 1.0), :lessthan)
         end
         blmo = Boscia.CubeBLMO(n, int_vars, bounds)
 
-        x, _, result = Boscia.solve(f, grad!, blmo, verbose =true)
+        x, _, result = Boscia.solve(f, grad!, blmo, verbose=true)
 
         @test x == round.(diffi)
         @test isapprox(f(x), f(result[:raw_solution]), atol=1e-6, rtol=1e-3)
@@ -59,14 +59,15 @@ diffi = Random.rand(Bool, n) * 0.6 .+ 0.3
         int_vars = collect(1:n)
         lbs = zeros(n)
         ubs = ones(n)
-        
+
         sblmo = Boscia.CubeSimpleBLMO(lbs, ubs, int_vars)
 
-        x, _, result = Boscia.solve(f, grad!, sblmo, lbs[int_vars], ubs[int_vars], int_vars, n, verbose =true)
+        x, _, result =
+            Boscia.solve(f, grad!, sblmo, lbs[int_vars], ubs[int_vars], int_vars, n, verbose=true)
 
         @test x == round.(diffi)
         @test isapprox(f(x), f(result[:raw_solution]), atol=1e-6, rtol=1e-3)
-    end 
+    end
 end
 
 
@@ -79,7 +80,7 @@ end
         @. storage = x - diffi
     end
 
-    int_vars = unique!(rand(collect(1:n), Int(floor(n/2))))
+    int_vars = unique!(rand(collect(1:n), Int(floor(n / 2))))
 
     @testset "Using SCIP" begin
         o = SCIP.Optimizer()
@@ -99,37 +100,38 @@ end
 
         sol = diffi
         sol[int_vars] = round.(sol[int_vars])
-        @test sum(isapprox.(x, sol, atol =1e-6, rtol=1e-2)) == n
+        @test sum(isapprox.(x, sol, atol=1e-6, rtol=1e-2)) == n
         @test isapprox(f(x), f(result[:raw_solution]), atol=1e-6, rtol=1e-3)
     end
 
-    @testset "Using Cube LMO" begin        
+    @testset "Using Cube LMO" begin
         bounds = Boscia.IntegerBounds()
-        for i in 1:n 
+        for i in 1:n
             push!(bounds, (i, 0.0), :greaterthan)
             push!(bounds, (i, 1.0), :lessthan)
         end
         blmo = Boscia.CubeBLMO(n, int_vars, bounds)
 
-        x, _, result = Boscia.solve(f, grad!, blmo, verbose =true)
+        x, _, result = Boscia.solve(f, grad!, blmo, verbose=true)
 
         sol = diffi
         sol[int_vars] = round.(sol[int_vars])
-        @test sum(isapprox.(x, sol, atol =1e-6, rtol=1e-2)) == n
+        @test sum(isapprox.(x, sol, atol=1e-6, rtol=1e-2)) == n
         @test isapprox(f(x), f(result[:raw_solution]), atol=1e-6, rtol=1e-3)
     end
 
     @testset "Using Cube Simple LMO" begin
         lbs = zeros(n)
         ubs = ones(n)
-        
+
         sblmo = Boscia.CubeSimpleBLMO(lbs, ubs, int_vars)
 
-        x, _, result = Boscia.solve(f, grad!, sblmo, lbs[int_vars], ubs[int_vars], int_vars, n, verbose =true)
+        x, _, result =
+            Boscia.solve(f, grad!, sblmo, lbs[int_vars], ubs[int_vars], int_vars, n, verbose=true)
 
         sol = diffi
         sol[int_vars] = round.(sol[int_vars])
-        @test sum(isapprox.(x, sol, atol =1e-6, rtol=1e-2)) == n
+        @test sum(isapprox.(x, sol, atol=1e-6, rtol=1e-2)) == n
         @test isapprox(f(x), f(result[:raw_solution]), atol=1e-6, rtol=1e-3)
     end
-end 
+end
diff --git a/examples/mps-example.jl b/examples/mps-example.jl
@@ -60,4 +60,3 @@ end
     x, _, result = Boscia.solve(f, grad!, lmo, verbose=true)
     @test f(x) <= f(result[:raw_solution])
 end
-
diff --git a/examples/mps-examples/mip-examples.jl b/examples/mps-examples/mip-examples.jl
@@ -45,7 +45,7 @@ function build_example(example, num_v)
     MOI.set(o, MOI.RawOptimizerAttribute("presolving/maxrounds"), 0)
 
     #trick to push the optimum towards the interior
-    vs = [FrankWolfe.compute_extreme_point(lmo, randn(n)) for _ in 1:num_v]   
+    vs = [FrankWolfe.compute_extreme_point(lmo, randn(n)) for _ in 1:num_v]
     # done to avoid one vertex being systematically selected
     unique!(vs)
 
@@ -62,10 +62,10 @@ function build_example(example, num_v)
     end
 
     function grad!(storage, x)
-        mul!(storage, length(vs)/max_norm * I, x)
+        mul!(storage, length(vs) / max_norm * I, x)
         storage .+= b_mps
         for v in vs
-            @. storage -= 1/max_norm * v
+            @. storage -= 1 / max_norm * v
         end
     end
 
@@ -94,7 +94,15 @@ test_instance = string("MPS ", example, " instance")
 @testset "$test_instance" begin
     println("Example $(example)")
     lmo, f, grad! = build_example(example, num_v)
-    x, _, result = Boscia.solve(f, grad!, lmo, verbose=true, print_iter = 10, fw_epsilon = 1e-1, min_node_fw_epsilon = 1e-3, time_limit=600)
+    x, _, result = Boscia.solve(
+        f,
+        grad!,
+        lmo,
+        verbose=true,
+        print_iter=10,
+        fw_epsilon=1e-1,
+        min_node_fw_epsilon=1e-3,
+        time_limit=600,
+    )
     @test f(x) <= f(result[:raw_solution])
 end
-
diff --git a/ext/BosciaHiGHSExt.jl b/ext/BosciaHiGHSExt.jl
@@ -7,5 +7,5 @@ const MOI = MathOptInterface
 const MOIU = MOI.Utilities
 
 import MathOptSetDistances as MOD
-    
+
 end # module
diff --git a/ext/BosciaSCIPExt.jl b/ext/BosciaSCIPExt.jl
@@ -12,7 +12,12 @@ import MathOptSetDistances as MOD
 Finds the best solution in the SCIP solution storage, based on the objective function `f`.
 Returns the solution vector and the corresponding best value.
 """
-function Boscia.find_best_solution(f::Function, o::SCIP.Optimizer, vars::Vector{MOI.VariableIndex}, domain_oracle)
+function Boscia.find_best_solution(
+    f::Function,
+    o::SCIP.Optimizer,
+    vars::Vector{MOI.VariableIndex},
+    domain_oracle,
+)
     sols_vec =
         unsafe_wrap(Vector{Ptr{Cvoid}}, SCIP.LibSCIP.SCIPgetSols(o), SCIP.LibSCIP.SCIPgetNSols(o))
     best_val = Inf
@@ -35,7 +40,7 @@ end
 Cleanup internal SCIP model
 """
 function Boscia.free_model(o::SCIP.Optimizer)
-    SCIP.SCIPfreeTransform(o)
+    return SCIP.SCIPfreeTransform(o)
 end
 
 """
@@ -44,5 +49,5 @@ Get solving tolerance.
 function Boscia.get_tol(o::SCIP.Optimizer)
     return MOI.get(o, MOI.RawOptimizerAttribute("numerics/feastol"))
 end
-    
+
 end # module
Original file line number	Diff line number	Diff line change
Expand Up		@@ -60,4 +60,3 @@ end
		x, _, result = Boscia.solve(f, grad!, lmo, verbose=true)
		@test f(x) <= f(result[:raw_solution])
		end
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,5 +7,5 @@ const MOI = MathOptInterface
		const MOIU = MOI.Utilities

		import MathOptSetDistances as MOD


		end # module