Generalizing single-level relaxations for bilevel linear programs

Aug 2026

Network Relaxations for Combinatorial Bilevel Optimization Under Linear Interactions

A new representation for problems in which leader-follower interactions are captured by linear inequalities involving binary leader decisions is introduced, which uses a layered decision-diagram network whose paths encode the leader’s choices and whose terminal values represent the follower’s optimal objective value.

Leonardo Lozano, David Bergman, A. Ciré · Operational Research · 1 citation

Preprint Sep 2026

A true single-level reformulation for pessimistic bilevel optimization

We propose a single-level reformulation (SLR) for the pessimistic bilevel optimization problem that does not rely on complementarity conditions or optimal value functions. For this reason, we refer to it as a true single-level reformulation (tSLR). A remarkable consequence is that this reformulation can satisfy the cla...

O. Stein, Alain B. Zemkoho · 1 citation

Open access Jul 2026

Multi-parametric approach for nonlinear bilevel optimization problems and beyond

One of the peculiar features of multi-parametric approach for bilevel programs is that most methods using this approach can be extended to tri-level (and generally to $k$-level) programs, which is not always the case with other non-heuristic solution methods. However, most of existing multi-parametric methods work well...

Addis Belete Zewde, Semu Mitiku Kassa · Reserche operationelle · 0 citations

Adaptive Scenario Partitioning for Stochastic Bilevel Linear Programs

The results show that basis-aware refinement and informative initial partitions substantially reduce solution times while keeping objective gaps small relative to the reference model, indicating that adaptive partitioning is a promising computational strategy for large-scale stochastic bilevel optimization.

Carlos Muñoz-Rey, Francisco Jara-Moroni · 0 citations

Preprint Aug 2026

Exact and Heuristic Methods for $\Gamma$-Robust Min-Max Problems

The results show that the heuristic closes the optimality gap for a significant portion of the considered instances and often practically outperforms both heuristic and exact benchmark approaches.

Yasmine Beck · 0 citations

Computing diverse solutions to optimization problems

This paper considers the problem of computing a pool of near-optimal solutions to a given optimization problem while maximizing their total pairwise distance, and focuses on Mixed-Integer Linear Programming problems where a subset of variables are binary and consider the Hamming distance over these variables.

Enrico Malaguti, Michele Monaci, P. Paronuzzi · 0 citations