Course

Integer Programming

Decision variables that must take integer or binary values: what integrality does to a linear program, how to express logical conditions such as at-most-one and only-if with binary variables, and what the bound from the linear relaxation establishes. Requires formulation and outcome classification, not the simplex method.

Start Integer Programming

Modules
1
Lessons
3
Skills
3
Starting here
Assumes 3 prior topics
  1. Module 1: Integer programming

    Integrality as one extra line in the program and a different problem underneath. The relaxation is where the central idea and the most common error both live.

      • Given a described situation or a stated program, the learner can say which variables require integrality and which do not, classify the program as pure integer, mixed-integer or binary, describe the feasible set integrality produces, and state which results about linear programs cease to apply.
      • Given a situation containing yes-or-no decisions and conditions relating them, the learner can define binary variables with stated meanings, write linear constraints expressing selection counts, implications and links between decisions and quantities, choose a defensible bound for a linking constraint, and verify that each constraint forbids what it is meant to forbid.
      • Given an integer program, the learner can form its linear relaxation, state the direction of the bound its optimum provides, use the bound to judge how far a candidate integer solution can be from optimal, draw the inferences an infeasible or integral relaxation licenses, and explain why rounding is not a solution method.

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3 required skills. If you reach a lesson without the background it assumes, you are pointed at the prerequisite first, and returned here afterwards.

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