arXiv subjects

Camille Grange

Publications and source records attributed to Camille Grange.

5 recordsLinked to original sources

arXiv2026

A spatio-temporal graph-based model for team sports analysis

Team sports represent complex phenomena characterized by both spatial and temporal dimensions, making their analysis inherently challenging. In this study, we examine team sports as complex systems, specifically focusing on the tactical aspects influenced by external constraints. To this end, we introduce a new generic graph-based model to analyze these phenomena. Specifically, we model a team sport's attacking play as a directed path containing absolute and relative ball carrier-centered spatial information, temporal information, and semantic information. We apply our model to union rugby, aiming to validate two hypotheses regarding the impact of the pedagogy provided by the coach on the one hand, and the effect of the initial positioning of the defensive team on the other hand. Preliminary results from data collected on six-player rugby from several French clubs indicate notable effects of these constraints. The model is intended to be applied to other team sports and to validate additional hypotheses related to team coordination patterns, including upcoming applications in basketball.

cs.SI↗

arXiv2025

The Human-GenAI Value Loop in Human-Centered Innovation: Beyond the Magical Narrative

Organizations across various industries are still exploring the potential of Generative Artificial Intelligence (GenAI) to enhance knowledge work. While innovation is often viewed as a product of individual creativity, it more commonly unfolds through a highly structured, collaborative process where creativity intertwines with knowledge work. However, the extent and effectiveness of GenAI in supporting this process remain open questions. Our study investigates this issue using a collaborative practice research approach focused on three GenAI-enabled innovation projects conducted over a year within three different organizations. We explored how, why, and when GenAI could be integrated into design sprints, a highly structured, collaborative, and human-centered innovation method. Our research identified challenges and opportunities in synchronizing AI capabilities with human intelligence and creativity. To translate these insights into practical strategies, we propose four recommendations for organizations eager to leverage GenAI to both streamline and bring more value to their innovation processes: (1) establish a collaborative intelligence value loop with GenAI; (2) build trust in GenAI, (3) develop robust data collection and curation workflows, and (4) cultivate a craftsmanship mindset.

cs.HC↗

arXiv2024

Quadratic versus Polynomial Unconstrained Binary Models for Quantum Optimization illustrated on Railway Timetabling

Quantum Approximate Optimization Algorithm (QAOA) is one of the most short-term promising quantum-classical algorithm to solve unconstrained combinatorial optimization problems. It alternates between the execution of a parametrized quantum circuit and a classical optimization. There are numerous levers for enhancing QAOA performances, such as the choice of quantum circuit meta-parameters or the choice of the classical optimizer. In this paper, we stress on the importance of the input problem formulation by illustrating it with the resolution of an industrial railway timetabling problem. Specifically, we present a generic method to reformulate any polynomial problem into a Polynomial Unconstrained Binary Optimization (PUBO) problem, with a specific formulation imposing penalty terms to take binary values when the constraints are linear. We also provide a generic reformulation into a Quadratic Unconstrained Binary Optimization (QUBO) problem. We then conduct a numerical comparison between the PUBO with binary penalty terms and the QUBO formulations proposed on a railway timetabling problem solved with QAOA. Our results illustrate that the PUBO reformulation outperforms the QUBO one for the problem at hand.

math.OC↗

arXiv2024

Moderate Exponential-time Quantum Dynamic Programming Across the Subsets for Scheduling Problems

Grover Search is currently one of the main quantum algorithms leading to hybrid quantum-classical methods that reduce the worst-case time complexity for some combinatorial optimization problems. Specifically, the combination of Quantum Minimum Finding (obtained from Grover Search) with dynamic programming has proved particularly efficient in improving the complexity of NP-hard problems currently solved by classical dynamic programming. For these problems, the classical dynamic programming complexity in $\mathcal{O}^*(c^n)$, where $\mathcal{O}^*$ denotes that polynomial factors are ignored, can be reduced by a hybrid algorithm to $\mathcal{O}^*(c_{quant}^n)$, with $c_{quant} < c$. In this paper, we provide a bounded-error hybrid algorithm that achieves such an improvement for a broad class of NP-hard single-machine scheduling problems for which we give a generic description. Moreover, we extend this algorithm to tackle the 3-machine flowshop problem. Our algorithm reduces the exponential-part complexity compared to the best-known classical algorithm, sometimes at the cost of an additional pseudo-polynomial factor.

quant-ph↗

arXiv2024

An introduction to variational quantum algorithms for combinatorial optimization problems

Noisy intermediate-scale quantum computers (NISQ computers) are now readily available, motivating many researchers to experiment with Variational Quantum Algorithms (VQAs). Among them, the Quantum Approximate Optimization Algorithm (QAOA) is one of the most popular one studied by the combinatorial optimization community. In this tutorial, we provide a mathematical description of the class of Variational Quantum Algorithms, assuming no previous knowledge of quantum physics from the readers. We introduce precisely the key aspects of these hybrid algorithms on the quantum side (parametrized quantum circuit) and the classical side (guiding function, optimizer). We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions. Finally, we discuss the recent literature on QAOA, highlighting several research trends.

math.OC↗