Solving a problem using MathOptInterface

In this example, we want to solve a binary-constrained knapsack problem:

\[\begin{aligned} \max \; & c^\top x \\ s.t. \; & w^\top x \le C \\ & x_i \in \{0,1\},\quad \forall i=1,\ldots,n \end{aligned}\]

Load the MathOptInterface module and define the shorthand MOI:

using MathOptInterface
const MOI = MathOptInterface

As an optimizer, we choose GLPK:

using GLPK
optimizer = GLPK.Optimizer()

Define the data

We first define the constants of the problem:

julia> c = [1.0, 2.0, 3.0]
3-element Vector{Float64}:
 1.0
 2.0
 3.0

julia> w = [0.3, 0.5, 1.0]
3-element Vector{Float64}:
 0.3
 0.5
 1.0

julia> C = 3.2
3.2

Add the variables

julia> x = MOI.add_variables(optimizer, length(c));

Set the objective

julia> MOI.set(
           optimizer,
           MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
           MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(c, x), 0.0),
       );

julia> MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MAX_SENSE)
Tip

MOI.ScalarAffineTerm.(c, x) is a shortcut for [MOI.ScalarAffineTerm(c[i], x[i]) for i = 1:3]. This is Julia's broadcast syntax in action, and is used quite often throughout MOI.

Add the constraints

We add the knapsack constraint and integrality constraints:

julia> MOI.add_constraint(
           optimizer,
           MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(w, x), 0.0),
           MOI.LessThan(C),
       );

Add integrality constraints:

julia> for x_i in x
           MOI.add_constraint(optimizer, x_i, MOI.ZeroOne())
       end

Optimize the model

julia> MOI.optimize!(optimizer)

Understand why the solver stopped

The first thing to check after optimization is why the solver stopped, e.g., did it stop because of a time limit or did it stop because it found the optimal solution?

julia> MOI.get(optimizer, MOI.TerminationStatus())
OPTIMAL::TerminationStatusCode = 1

Looks like we found an optimal solution!

Understand what solution was returned

julia> MOI.get(optimizer, MOI.ResultCount())
1

julia> MOI.get(optimizer, MOI.PrimalStatus())
FEASIBLE_POINT::ResultStatusCode = 1

julia> MOI.get(optimizer, MOI.DualStatus())
NO_SOLUTION::ResultStatusCode = 0

Query the objective

What is its objective value?

julia> MOI.get(optimizer, MOI.ObjectiveValue())
6.0

Query the primal solution

And what is the value of the variables x?

julia> MOI.get(optimizer, MOI.VariablePrimal(), x)
3-element Vector{Float64}:
 1.0
 1.0
 1.0