9 papers
GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal -sparse GLMs
Jiachang Liu, Andrea Lodi, Soroosh Shafiee
We investigate the problem of certifying optimality for sparse generalized linear models (GLMs), where sparsity is enforced through a cardinality constraint. While Branch-and-Bound…
SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
Matteo Francobaldi, Michele Lombardi, Andrea Lodi
Artificial Intelligence systems are increasingly deployed in settings where ensuring robustness, fairness, or domain-specific properties is essential for regulation compliance and…
Cutting Planes for Binarized Network Flow Problems
Pierre Bonami, Sanjeeb Dash, Anton Derkach +1
We consider integer programming problems with bounded general-integer variables belonging to the general class of network flow problems. For those, we computationally investigate t…
Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids
Nathan Cho, Andrea Lodi, Anna Scaglione
We jointly schedule electric trucks and stationary batteries in an isolated microgrid with solar and fossil generation. Truck routes determine when their batteries can charge or re…
How hard is learning to cut? Trade-offs and sample complexity
Sammy Khalife, Andrea Lodi
In the recent years, branch-and-cut algorithms have been the target of data-driven approaches designed to enhance the decision making in different phases of the algorithm such as b…
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
Jiachang Liu, Soroosh Shafiee, Andrea Lodi
This paper investigates the problem of certifying optimality for sparse generalized linear models (GLMs), where sparsity is enforced through an cardinality constraint. Whi…
The Differentiable Feasibility Pump
Matteo Cacciola, Alexandre Forel, Antonio Frangioni +1
Although nearly 20 years have passed since its conception, the feasibility pump algorithm remains a widely used heuristic to find feasible primal solutions to mixed-integer linear…
Algorithmic Collusion And The Minimum Price Markov Game
Igor Sadoune, Marcelin Joanis, Andrea Lodi
This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such a…
One-for-many Counterfactual Explanations by Column Generation
Andrea Lodi, Jasone Ramírez-Ayerbe
In this paper, we consider the problem of generating a set of counterfactual explanations for a group of instances, with the one-for-many allocation rule, where one explanation is…