Up until now in this series, we’ve talked about strict linear programming — where the objective function, constraints and decision variables have all been linear and continuous. This linear set up comes with some really nice properties, but it isn’t very flexible. In this article, I’ll discuss how we can allow for discrete decision variables using a tool called integer linear programming (ILP).
This is the fourth article in a series I’m writing on linear programming. The other articles (including an introduction — in case you aren’t familiar with linear programming) can be found here:
Linear Programming
In this article we’ll be covering the following topics:
- When discrete decision variables are needed
- How the branch and bound algorithm solves integer linear programming problems
- The pros and cons of integer linear programming…