Range Constraints and Solving Integer Linear Programming

Polynomial formulation for range constraints

Let $f$ be a polynomial expression of binary variables. A range constraint has the form $l\leq f\leq u$ with $l<u$. Our goal is to design a polynomial expression that takes the minimum value 0 if and only if the range constraint is satisfied.

The key idea is to introduce an auxiliary integer variable $a$ that takes values in the range $[l,u]$. Consider the following expression:

\[\begin{aligned} g &= (f-a)^2 \end{aligned}\]

This expression $g$ takes the minimum value 0 exactly when $f=a$. Since $a$ can take any integer value in $[l,u]$, the expression $g$ achieves 0 if and only if $f$ itself takes an integer value within the same range.

Using this auxiliary-variable technique, PyQBPP implements range constraints via the between() function.

Solving Integer Linear Programming

An instance of integer linear programming consists of an objective function and multiple linear constraints. For example, the following integer linear program has two variables, one objective, and two constraints:

\[\begin{aligned} \text{Maximize: } & & & 5x + 4y \\ \text{Subject to: } & && 2x + 3y \le 24 \\ & & & 7x + 5y \le 54 \end{aligned}\]

The optimal solution of this problem is $x=4$, $y=5$, with the objective value $40$.

The following PyQBPP program finds this optimal solution using the Easy Solver:

from pyqbpp import var_int, between, EasySolver

x = between(var_int("x"), 0, 10)
y = between(var_int("y"), 0, 10)
f = 5 * x + 4 * y
c1 = between(2 * x + 3 * y, 0, 24)
c2 = between(7 * x + 5 * y, 0, 54)
g = -f + 100 * (c1 + c2)
g.simplify_as_binary()

solver = EasySolver(g)
solver.time_limit(1.0)
sol = solver.search()

print(f"x = {sol.eval(x)}, y = {sol.eval(y)}")
print(f"f = {sol.eval(f)}")
print(f"c1 = {sol.eval(c1)}, c2 = {sol.eval(c2)}")
print(f"2x+3y = {sol.eval(c1.body)}, 7x+5y = {sol.eval(c2.body)}")

In this program,

  • f represents the objective function,
  • c1 and c2 represent the range constraints created using between(), and
  • g combines them into a single optimization expression.

Since the goal is maximization, the objective is negated as -f. The constraints c1 and c2 are penalized with a weight of 100 to ensure they are satisfied with high priority.

An Easy Solver instance is created for g, and a search is performed with a time limit of 1.0 seconds. After obtaining the optimal solution sol, the program prints the values of x, y, f, c1, c2, and the constraint body expressions.

The program outputs:

x = 4, y = 5
f = 40
c1 = 0, c2 = 0
2x+3y = 23, 7x+5y = 53

Here,

  • c1 is the penalty for the constraint 0 <= 2x + 3y <= 24, and
  • c1.body represents the linear expression 2x + 3y.

We can confirm that the solver correctly finds the optimal solution.

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