Merge pull request #199 from joaquimg/jg/fixdoc

Cleanup docs

docs/src/examples/DTMP_example1.jl

@@ -60,7 +60,7 @@ set_silent(model)
 
 optimize!(model)
 
-# Auto testing
+# Results
 
 value(x)
 

docs/src/examples/FOBP_example2.jl

@@ -25,7 +25,7 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 
@@ -82,15 +82,31 @@ BilevelJuMP.set_primal_upper_bound_hint(y, 9)
 
 optimize!(model)
 
-# Test expected results
-
-@test objective_value(model) ≈ 3 * (3.5 * 8 / 15) + (8 / 15) atol = 1e-4
-@test BilevelJuMP.lower_objective_value(model) ≈ -3.5 * 8 / 15 atol = 1e-4
-@test objective_value(Lower(model)) ≈ -3.5 * 8 / 15 atol = 1e-4
-@test value(x) ≈ 3.5 * 8 / 15 atol = 1e-4
-@test value(y) ≈ 8 / 15 atol = 1e-4
-@test value(u1) ≈ 3.5 * 8 / 15 atol = 1e-4
-@test value(l1) ≈ 4.5 * 8 / 15 atol = 1e-4
-@test dual(l1) ≈ [0] atol = 1e-4
-@test dual(l3) ≈ [0] atol = 1e-4
+objective_value(model)
+
+objective_value(Lower(model))
+
+value(x)
+
+value(y)
+
+value(u1)
+
+value(l1)
+
+dual(l1)
+
+dual(l3)
+
+# Test expected results #src
+
+@test objective_value(model) ≈ 3 * (3.5 * 8 / 15) + (8 / 15) atol = 1e-4 #src
+@test BilevelJuMP.lower_objective_value(model) ≈ -3.5 * 8 / 15 atol = 1e-4 #src
+@test objective_value(Lower(model)) ≈ -3.5 * 8 / 15 atol = 1e-4 #src
+@test value(x) ≈ 3.5 * 8 / 15 atol = 1e-4 #src
+@test value(y) ≈ 8 / 15 atol = 1e-4 #src
+@test value(u1) ≈ 3.5 * 8 / 15 atol = 1e-4 #src
+@test value(l1) ≈ 4.5 * 8 / 15 atol = 1e-4 #src
+@test dual(l1) ≈ [0] atol = 1e-4 #src
+@test dual(l3) ≈ [0] atol = 1e-4 #src
 

docs/src/examples/FOBP_example3.jl

@@ -33,15 +33,15 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 
 # Global variables
 
 I = 7 # maximum literals
 clauses = [[1, 2, 3], [-1, -4, 3], [7, -6, 4], [5, 6, 7]]
-atol = 1e-6
+
 # First we need to create all of the variables in the upper and lower problems:
 
 # Upper level variables
@@ -99,15 +99,40 @@ end
 # Dempe.
 
 optimize!(model)
+
+# 
 primal_status(model)
+
+#
+
 termination_status(model)
 
-# Auto testing
-@test objective_value(model) ≈ -1 atol = atol
-@test value.(x) ≈ zeros(I) atol = atol
-@test value.(ya) ≈ ones(I) atol = atol
-@test value.(yb) ≈ zeros(I) atol = atol
-@test value(z) ≈ 1 atol = atol
-# @show dual.(b1) #≈ 6 atol=atol
-# @show dual.(b2) #≈ 2 atol=atol
-# @show dual.(b3) #≈ 2 atol=atol
+# Results
+
+objective_value(model)
+
+#
+
+value.(x)
+
+#
+
+value.(ya)
+
+#
+
+value.(yb)
+
+#
+
+value(z)
+
+
+# Auto testing #src
+atol = 1e-4 #src
+@test objective_value(model) ≈ -1 atol = atol #src
+@test value.(x) ≈ zeros(I) atol = atol #src
+@test value.(ya) ≈ ones(I) atol = atol #src
+@test value.(yb) ≈ zeros(I) atol = atol #src
+@test value(z) ≈ 1 atol = atol #src
+

docs/src/examples/FOBP_example4.jl

@@ -20,13 +20,10 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 
-# Global variables
-atol = 1e-3
-
 # First we need to create all of the variables in the upper and lower problems:
 
 # Upper level variables
@@ -57,10 +54,21 @@ atol = 1e-3
 # Dempe.
 
 optimize!(model)
+
 primal_status(model)
+
 termination_status(model)
 
-# Auto testing
-@test objective_value(model) ≈ 0 atol = atol
-@test value(x) ≈ 0 atol = atol
-@test value(y) ≈ 0 atol = atol
+# Results
+
+objective_value(model)
+
+value(x)
+
+value(y)
+
+# Auto testing #src
+atol = 1e-3 # src
+@test objective_value(model) ≈ 0 atol = atol #src
+@test value(x) ≈ 0 atol = atol #src
+@test value(y) ≈ 0 atol = atol #src

docs/src/examples/FOBP_example5.jl

@@ -22,13 +22,10 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 
-# Global variables
-atol = 1e-3
-
 # First we need to create all of the variables in the upper and lower problems:
 
 # Upper level variables
@@ -53,11 +50,15 @@ atol = 1e-3
 @constraint(Lower(model), -4x + 5y <= 10)
 @constraint(Lower(model), -4x - 5y <= 10)
 
-# Initial Starting conditions  #src
-
 # Now we can solve the problem and verify the solution again that reported by
 # Dempe.
 
 optimize!(model)
+
 primal_status(model)
+
 termination_status(model)
+
+value(x)
+
+value(y)

docs/src/examples/PHTP_example1.jl

@@ -23,13 +23,10 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 
-# Global variables
-atol = 1e-3
-
 # First we need to create all of the variables in the upper and lower problems:
 
 # Upper level variables
@@ -62,8 +59,15 @@ atol = 1e-3
 # the book.
 
 optimize!(model)
+
 primal_status(model)
+
 termination_status(model)
 
-@test value(x) ≈ 1 atol = atol
-@test value(y) ≈ 0 atol = atol
+value(x)
+
+value(y)
+
+atol = 1e-3 #src
+@test value(x) ≈ 1 atol = atol #src
+@test value(y) ≈ 0 atol = atol #src

docs/src/examples/PHTP_example2.jl

@@ -24,7 +24,7 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 
@@ -61,12 +61,23 @@ model = BilevelModel(Ipopt.Optimizer; mode = BilevelJuMP.ProductMode(1e-9))
 # Now we can solve the problem and verify the solution again that reported by the book
 
 optimize!(model)
+
 primal_status(model)
+
 termination_status(model)
 
-# Auto testing
-@test objective_value(model) ≈ 0 atol = 1e-3
-sol = vcat(value.(x), value.(y))
-@test sol ≈ [0; 0; -10; -10] || sol ≈ [0; 30; -10; 10] #atol=1e-3
+objective_value(model)
+
+value.(x)
+
+value.(y)
+
+# Auto testing #src
+@test objective_value(model) ≈ 0 atol = 1e-3 #src
+sol = vcat(value.(x), value.(y)) #src
+@test sol ≈ [0; 0; -10; -10] || sol ≈ [0; 30; -10; 10] #atol=1e-3 #src
 
-# # Like any other optimization problem, there is a chance in bilevel optimization to find multiple solutions with the same optimal value; based on the inherent stochasticity of the algorithm and random seed, we are expecting two optimal solutions for this problem. 
+# # Like any other optimization problem, there is a chance in bilevel
+# optimization to find multiple solutions with the same optimal value;
+# based on the inherent stochasticity of the algorithm and random seed,
+# we are expecting two optimal solutions for this problem. 

docs/src/examples/SOCBLP_example1.jl

@@ -32,7 +32,7 @@
 
 using BilevelJuMP
 using Ipopt
-using Test
+using Test #src
 
 model = BilevelModel(
     () -> MOI.Bridges.Constraint.SOCtoNonConvexQuad{Float64}(Ipopt.Optimizer());
@@ -90,12 +90,16 @@ end
 # Now we can solve the problem and verify the solution again that reported by
 
 optimize!(model)
+
 primal_status(model)
+
 termination_status(model)
 
+objective_value(model)
+
 value.(y)
 
-@test objective_value(model) ≈ 12 atol = 1e-1
-@test value(x) ≈ 6 atol = 1e-3
-@test value(y[2]) >= 0 - 1e-3
-@test value(y[1]) - value(y[2]) ≈ 2 atol = 1e-3
+@test objective_value(model) ≈ 12 atol = 1e-1 #src
+@test value(x) ≈ 6 atol = 1e-3 #src
+@test value(y[2]) >= 0 - 1e-3 #src
+@test value(y[1]) - value(y[2]) ≈ 2 atol = 1e-3 #src