We study the vehicle routing problem with stochastic demands (VRPSD), an important variant of the classical capacitated vehicle routing problem in which customer demands are modeled as random variables. We develop the first algorithm for the VRPSD in the case where the demands are given by an empirical probability distribution of scenarios — a data-driven … Read more
Binarized neural networks (BNNs) are feedforward neural networks with binary weights and activation functions. In the context of using a BNN for classification, the verification problem seeks to determine whether a small perturbation of a given input can lead it to be misclassified by the BNN, and the robustness of the BNN can be measured … Read more
We introduce a novel technique to generate Benders’ cuts from a conic relaxation (“corner”) derived from a basis of a higher-dimensional polyhedron that we aim to outer approximate in a lower-dimensional space. To generate facet-defining inequalities for the epigraph associated to this corner, we develop a computationally-efficient algorithm based on a compact reverse polar formulation … Read more
A polytope $P\subseteq [0,1]^n$ is said to have the \emph{persistency} property if for every vector $c\in \R^{n}$ and every $c$-optimal point $x\in P$, there exists a $c$-optimal integer point $y\in P\cap \{0,1\}^n$ such that $x_i = y_i$ for each $i \in \{1,\dots,n\}$ with $x_i \in \{0,1\}$. In this paper, we consider a relaxation of the … Read more
Binary bilevel programs are notoriously difficult to solve due to the absence of strong and efficiently computable relaxations. In this work, we introduce a novel single-level reformulation of these programs by leveraging a network flow-based representation of the follower’s value function, utilizing decision diagrams and linear programming duality. This approach enables the development of scalable … Read more
Lower bounds on minimization problems are essential for convergence of both branching-based and iterative solution methods for optimization problems. They are also required for evaluating the quality of feasible solutions by providing conservative optimality gaps. We provide an analytical lower bound for a class of quadratic optimization problems with binary decision variables. In contrast to … Read more
This paper studies the problems of partitioning the vertices of a graph G = (V,E) into (or covering with) a minimum number of low-diameter clusters from the lenses of approximation algorithms and integer programming. Here, the low-diameter criterion is formalized by an s-club, which is a subset of vertices whose induced subgraph has diameter at … Read more
We present column elimination as a general framework for solving (large-scale) integer programming problems. In this framework, solutions are represented compactly as paths in a directed acyclic graph. Column elimination starts with a relaxed representation, that may contain infeasible paths, and solves a constrained network flow over the graph to find a solution. It then … Read more
University students benefit academically, personally and professionally from an expansion of their in-class social network. To facilitate this, we present a novel and broadly applicable optimization approach that exposes individuals to as many as possible peers that they do not know. This novel class of ‘social seating assignment problems’ is parameterized by the social network, … Read more
This tutorial provides an introduction to the use of decision diagrams for solving discrete optimization problems. A decision diagram is a graphical representation of the solution space, representing decisions sequentially as paths from a root node to a target node. By merging isomorphic subgraphs (or equivalent subproblems), decision diagrams can compactly represent an exponential solution … Read more