vidalt

Thibaut Vidal's repositories

HGS-CVRP

Modern implementation of the hybrid genetic search (HGS) algorithm specialized to the capacitated vehicle routing problem (CVRP). This code also includes an additional neighborhood called SWAP*.

Language:C++MIT305 11 17

BA-Trees

Born-Again Tree Ensembles: Transforms a random forest into a single, minimal-size, tree with exactly the same prediction function in the entire feature space (ICML 2020).

Language:C++MIT62 4 4

HGS-CARP

Hybrid Genetic Search for Arc Routing Problems. From "Node, edge, arc routing and turn penalties : Multiple problems - One neighborhood extension, Operations Research, 65(4), 2017", by Thibaut Vidal.

Language:C++GPL-3.032 9 3

OCEAN

OCEAN: Optimal Counterfactual Explanations in Tree Ensembles (ICML 2021)

Language:PythonMIT20 20

Library of Split algorithms, including the O(n) (linear time) algorithm for the CVRP, from "Vidal, Thibaut (2016). Technical note: Split algorithm in O(n) for the capacitated vehicle routing problem. Computers & Operations Research. 69, 40–47"

Language:C++GPL-3.016 40

HGS-TDCARP

Source code associated with the paper: "Vidal, T., Martinelli, R., Pham, T. A., & Hà, M. H. (2021). Arc routing with time-dependent travel times and paths. Transportation Science."

Language:C++GPL-3.015 2 1

PDTSP

Code and data from the paper "Exponential-size neighborhoods for the pickup-and-delivery traveling salesman problem", authored by T. Pacheco, R. Martinelli, A. Subramanian, T. Toffolo, and T. Vidal

Language:C++MIT15 20

DRAFT

DRAFT : Dataset Reconstruction Attack From Trained ensembles. Source code associated with the paper "Trained Random Forests Completely Reveal your Dataset (ICML'24, forthcoming)" authored by Julien Ferry, Ricardo Fukasawa, Timothée Pascal, and Thibaut Vidal

Language:PythonMIT1200

Decision-Trees

Simple greedy algorithm (CART limited to a fixed depth) for decision tree construction. Used for the course INF2980 (Metaheuristics) in PUC-Rio.

Language:C++GPL-3.07 60

Hard-Margin-SVM

Code and data from the paper "Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts" by I. Santana, B. Serrrano, M. Schiffer, and T. Vidal

Language:C++MIT4 30

Districting-Routing

Source code associated with the paper "Deep Learning for Data-Driven Districting-and-Routing", authored by A. Ferraz, Q. Cappart, and T. Vidal

Language:PythonMIT2 30

Optimal-SBM

This project contains the source code and data needed to reproduce all the experiments of the paper "Community Detection in the Stochastic Block Model by Mixed Integer Programming", authored by Breno Serrano and Thibaut Vidal

Language:OpenEdge ABLMIT2 10

Decision-Diagrams

Source code associated with the paper "Optimal Decision Diagrams for Classification", authored by A.M. Florio, P. Martins, M. Schiffer, T. Serra, and T. Vidal, presented at AAAI 2023

Language:PythonMIT100

HGS-SSP

Code and data from the paper: "Mecler, J., Subramanian, A., & Vidal, T. (2021). A simple and effective hybrid genetic search for the job sequencing and tool switching problem. Computers & Operations Research, 127, 105153."

Language:C++100

Influence-Optimization

Code and data from the paper "Influence Optimization in Networks: New Formulations and Valid Inequalities", authored by V. Ferreira, A. Pessoa, and T. Vidal

Language:Julia1 30

AssortativeSBM

Source code of AC-DC-SBM, from "Assortative-Constrained Stochastic Block Models", Gribel, Vidal and Gendreau, ICPR 2020

Language:Julia000

hg-means

Source code associated to the paper "Gribel, D., & Vidal, T. (2019). HG-means: A scalable hybrid metaheuristic for minimum sum-of-squares clustering. Pattern Recognition, 88, 569-583."

Language:C++GPL-3.0000