We study a delivery strategy for last-mile deliveries in urban areas which combines freight transportation with mass mobility systems with the goal of creating synergies contrasting negative externalities caused by transportation. The idea is to use the residual capacity on public transport means for moving freights within the city. In particular, the system is such … Read more
We tackle the problem of coordinating a three-echelon last-mile delivery system. In the first echelon, trucks transport parcels from distribution centres outside the city to public transport stops. In the second echelon, the parcels move on public transport and reach the city centre. In the third echelon, zero-emission vehicles pick up the parcels at public … Read more
In this work we study a particular way of dealing with interference in combinatorial optimization models representing wireless communication networks. In a typical wireless network, co-channel interference occurs whenever two overlapping antennas use the same frequency channel, and a less critical interference is generated whenever two overlapping antennas use adjacent channels. This motivates the formulation … Read more
In this work, we study the \emph{orientation model} for vertex coloring problems with the aim of finding partial descriptions of the associated polytopes. We present new families of valid inequalities, most of them supported by paths of the input graph. We develop facet-generating procedures for the associated polytopes, which we denominate \emph{path-lifting procedures}. Given a … Read more
A proper coloring of a given graph is an assignment of colors (integer numbers) to its vertices such that two adjacent vertices receives di different colors. This paper studies the Minimum Sum Coloring Problem (MSCP), which asks for fi nding a proper coloring while minimizing the sum of the colors assigned to the vertices. This paper presents … Read more
Poisoning attack is one of the attack types commonly studied in the field of adversarial machine learning. The adversary generating poison attacks is assumed to have access to the training process of a machine learning algorithm and aims to prevent the algorithm from functioning properly by injecting manipulative data while the algorithm is being trained. … Read more
We consider mixed-integer linear quantile minimization problems that yield large-scale problems that are very hard to solve for real-world instances. We motivate the study of this problem class by two important real-world problems: a maintenance planning problem for electricity networks and a quantile-based variant of the classic portfolio optimization problem. For these problems, we develop … Read more
\(\) This paper introduces HALLaR, a new first-order method for solving large-scale semidefinite programs (SDPs) with bounded domain. HALLaR is an inexact augmented Lagrangian (AL) method where the AL subproblems are solved by a novel hybrid low-rank (HLR) method. The recipe behind HLR is based on two key ingredients: 1) an adaptive inexact proximal point … Read more
Consider a semidefinite program (SDP) involving an $n\times n$ positive semidefinite matrix $X$. The Burer-Monteiro method consists in solving a nonconvex program in $Y$, where $Y$ is an $n\times p$ matrix such that $X = Y Y^T$. Despite nonconvexity, Boumal et al. showed that the method provably solves generic equality-constrained SDP’s when $p > \sqrt{2m}$, … Read more
Given an affine space of matrices L and a matrix \theta in L, consider the problem of finding the closest rank deficient matrix to \theta on L with respect to the Frobenius norm. This is a nonconvex problem with several applications in estimation problems. We introduce a novel semidefinite programming (SDP) relaxation, and we show … Read more