Indicator Cuts for Benders Decomposition with Mixed-Integer Subproblems

Classical Benders decomposition fails when the subproblem is a mixed-integer program, due to the absence of strong duality. We propose a novel class of dual-free indicator cuts that are applicable to all Benders-decomposable problems with a pure-integer master problem and mixed-integer linear programming (MILP) subproblems. These cuts are derived from the monotonicity property of the … Read more

Integrating Power Profile Optimization with Timetabling for Underground Train Networks

We study energy-efficient operation of underground train networks, where energy from regenerative braking is usable only if another train in the same electrically isolated subnetwork accelerates simultaneously. Timetabling models for this setting typically fix one velocity profile per leg and running time, which limits the matching of braking and accelerating phases. We drop this assumption … Read more

Convex Hulls of Binary Reflected Gray Code Intervals

The binary reflected Gray code orders the vertices of the unit hypercube along a Hamiltonian path in which consecutive vertices differ in exactly one coordinate. While Gray codes have been extensively studied from a combinatorial perspective, much less is known about the polyhedral structure of convex hulls of contiguous subpaths of this order. This paper … Read more

Finding Short Paths on Simple Polytopes

We prove that computing a shortest monotone path to the optimum of a linear program over a simple polytope is NP-hard, thus resolving a 2022 open question of De Loera, Kafer, and Sanit\`{a}. As a consequence, finding a shortest sequence of pivots to an optimal basis with the simplex method is NP-hard. In fact, we … Read more

Hardness of some optimization problems over correlation polyhedra

We prove the NP-hardness, using Karp reductions, of some problems related to the correlation polytope and its corresponding cone, spanned by all of the n×n rank-one matrices over {0, 1}. The problems are: membership, rank of the decomposition, and a “relaxed rank” obtained from relaxing the zero-norm expression for the rank to an ℓ1 norm. … Read more

Optimization over Trained Neural Networks: Going Large with Gradient-Based Algorithms

When optimizing a nonlinear objective, one can employ a neural network as a surrogate for the nonlinear function. However, the resulting optimization model can be time-consuming to solve globally with exact methods. As a result, local search that exploits the neural-network structure has been employed to find good solutions within a reasonable time limit. For … Read more

Dimensionality Reduction in Bilevel Linear Programming

We consider bilevel programs that involve a leader, who first commits to a mixed-integer decision, and a follower, who observes this decision and then responds rationally by solving a linear program (LP). Standard approaches often reformulate these bilevel optimization problems as single-level mixed-integer programs by exploiting the follower’s LP optimality conditions. These reformulations introduce either … Read more

Implied Integrality in Mixed-Integer Optimization

Implied-integer detection is a well-known presolving technique that is used by many Mixed-Integer Linear Programming solvers. Informally, a variable is said to be implied integer if its integrality is enforced implicitly by integrality of other variables and the constraints of a problem. In this work we formalize the definition of implied integrality by taking a … Read more

On parametric formulations for the Asymmetric Traveling Salesman Problem

The traveling salesman problem is a widely studied classical combinatorial problem for which there are several integer linear formulations. In this work, we consider the Miller-Tucker-Zemlin (MTZ), Desrochers-Laporte (DL) and Single Commodity Flow (SCF) formulations. We argue that the choice of some parameters of these formulations is arbitrary and, therefore, there are families of formulations … Read more