A Note on the Complexity of Bilevel Linear Programs in Fixed Dimensions

TL;DR

This paper investigates the computational complexity of bilevel linear programs with fixed numbers of variables or constraints, establishing new polynomial solvability results and hardness proofs that clarify the problem landscape.

Contribution

It proves that BLPs are polynomially solvable when the number of follower constraints is fixed, and shows that the pessimistic case is harder than the optimistic one under similar conditions.

Findings

BLPs are polynomially solvable with fixed follower constraints.

Pessimistic BLPs with fixed follower variables are strongly NP-hard.

Pessimistic formulation is more complex than optimistic under similar assumptions.

Abstract

Bilevel linear programs (BLPs) form a class of hierarchical decision-making problems in which both the upper-level and the lower-level decision-makers, known as the leader and the follower, respectively, solve linear optimization problems. It is well-known that general BLPs are strongly $NP$-hard, even when the leader's and the follower's objective functions are exact opposites. However, the complexity classification of BLPs remains incomplete when one of the decision-makers has a fixed number of variables or constraints. In particular, it has been shown that optimistic BLPs are polynomially solvable when the number of follower variables is fixed, whereas both optimistic and pessimistic BLPs remain $NP$-hard even with a single leader variable and no upper-level constraints. In this note, we close the remaining gap in this complexity landscape. Specifically, we prove that BLPs are polynomially solvable in both the optimistic and the pessimistic settings when the number of follower constraints is fixed. In contrast, we also show that the pessimistic problem with a fixed number of follower variables is strongly $NP$-hard. To the best of our knowledge, this is the first result demonstrating that, under comparable assumptions, the pessimistic formulation is one complexity class harder than its optimistic counterpart.