The knapsack problem

Formulate and solve a simple knapsack problem:

max sum(p_j x_j)
 st sum(w_j x_j) <= C
    x binary

using JuMP
import HiGHS
import Test

function example_knapsack(; verbose = true)
    profit = [5, 3, 2, 7, 4]
    weight = [2, 8, 4, 2, 5]
    capacity = 10
    model = Model(HiGHS.Optimizer)
    @variable(model, x[1:5], Bin)
    # Objective: maximize profit
    @objective(model, Max, profit' * x)
    # Constraint: can carry all
    @constraint(model, weight' * x <= capacity)
    # Solve problem using MIP solver
    optimize!(model)
    if verbose
        println("Objective is: ", objective_value(model))
        println("Solution is:")
        for i in 1:5
            print("x[$i] = ", value(x[i]))
            println(", p[$i]/w[$i] = ", profit[i] / weight[i])
        end
    end
    Test.@test termination_status(model) == OPTIMAL
    Test.@test primal_status(model) == FEASIBLE_POINT
    Test.@test objective_value(model) == 16.0
    return
end

example_knapsack()

Presolving model
1 rows, 5 cols, 5 nonzeros
1 rows, 4 cols, 4 nonzeros
Objective function is integral with scale 1

Solving MIP model with:
   1 rows
   4 cols (4 binary, 0 integer, 0 implied int., 0 continuous)
   4 nonzeros

( 0.0s) Starting symmetry detection
( 0.0s) No symmetry present

        Nodes      |    B&B Tree     |            Objective Bounds              |  Dynamic Constraints |       Work
     Proc. InQueue |  Leaves   Expl. | BestBound       BestSol              Gap |   Cuts   InLp Confl. | LpIters     Time

         0       0         0   0.00%   18              -inf                 inf        0      0      0         0     0.0s

Solving report
  Status            Optimal
  Primal bound      16
  Dual bound        16
  Gap               0% (tolerance: 0.01%)
  Solution status   feasible
                    16 (objective)
                    0 (bound viol.)
                    0 (int. viol.)
                    0 (row viol.)
  Timing            0.00 (total)
                    0.00 (presolve)
                    0.00 (postsolve)
  Nodes             1
  LP iterations     1 (total)
                    0 (strong br.)
                    0 (separation)
                    0 (heuristics)
Objective is: 16.0
Solution is:
x[1] = 1.0, p[1]/w[1] = 2.5
x[2] = 0.0, p[2]/w[2] = 0.375
x[3] = -0.0, p[3]/w[3] = 0.5
x[4] = 1.0, p[4]/w[4] = 3.5
x[5] = 1.0, p[5]/w[5] = 0.8