Update tsp_lazy_constraints.jl

docs/src/tutorials/algorithms/tsp_lazy_constraints.jl

@@ -116,15 +116,15 @@ import Plots
 # The vertices are assumed to be randomly distributed in the Euclidean space;
 # thus, the weight (distance) of each edge is defined as follows.
 
-function generate_distance_matrix(n; random_seed = 1234)
+function generate_distance_matrix(n; random_seed = 1)
  rng = Random.MersenneTwister(random_seed)
  X = 100 * rand(rng, n)
  Y = 100 * rand(rng, n)
  d = [sqrt((X[i] - X[j])^2 + (Y[i] - Y[j])^2) for i in 1:n, j in 1:n]
  return X, Y, d
 end
 
-n = 40
+n = 20
 X, Y, d = generate_distance_matrix(n)
 
 # For the JuMP model, we first initialize the model object. Then, we create the
@@ -252,7 +252,6 @@ function subtour_elimination_callback(cb_data)
  if !(1 < length(cycle) < n)
  return # Only add a constraint if there is a cycle
  end
- println("Found cycle of length $(length(cycle))")
  S = [(i, j) for (i, j) in Iterators.product(cycle, cycle) if i < j]
  con = @build_constraint(
  sum(lazy_model[:x][i, j] for (i, j) in S) <= length(cycle) - 1,