Conférences - Recherche opérationnelle - États-Unis

This workshop aims to bring together the essential communities from mathematics and engineering to address forthcoming issues central to our future transportation systems. It will bring together the operations research experts developing the next generation of fleet dispatching algorithms vital to mobility on demand services, the transportation engineers responsible for planning the future of our public and private transportation systems, and the modelers responsible for predicting new congestion dynamics at local and city scales. The workshop will also explore how these systems will operate in city, suburb, and rural contexts to support mobility needs for all.

Part of the Long Program Mathematical Challenges and Opportunities for Autonomous Vehicles

Électrotechnique,    Robotique, automation & mécatronique

Institute for Operations Research and the Management Sciences (informs)

Institute of Pure and Applied Mathematics (IPAM), UCLA

In recent years, deep learning has significantly improved the fields of computer vision, natural language processing and speech recognition. Beyond these traditional fields, deep learning has been expended to quantum chemistry, physics, neuroscience, and more recently to combinatorial optimization (CO). Well-known CO problems are Travelling Salesman Problem, assignment problems, routing, planning, Bayesian search, and scheduling. CO is basically used every day in finance and revenue management, transportation, manufacturing, supply chain, public policy, hardware design, computing and information technology. The workshop will bring together experts in mathematics (optimization, graph theory, sparsity, combinatorics, statistics), CO (assignment problems, routing, planning, Bayesian search, scheduling), machine learning (deep learning, supervised, self-supervised and reinforcement learning) and specific applicative domains (e.g. finance, transportation, hardware design, computing and information technology) to establish the current state of these emerging techniques and discuss the next directions. Besides, such generalization of deep learning techniques to CO problems will also push forward the mathematical analysis of the properties of these learning systems like generalization and transfer, stochastic optimization and dynamic predictivity that make the success of these techniques.

Théorie des graphes et combinatoire,    Réseaux de neurones et intelligence artificielle, apprentissage automatique

Society for Industrial and Applied Mathematics (SIAM)

The SIAM Conference on Optimization will feature the latest research on the theory, algorithms, software, and applications of optimization. A particular emphasis will be put on applications of optimization in AI and data science, quantum computing, health care, finance, aeronautics, control, operations research, and other areas of science and engineering. The conference brings together mathematicians, operations researchers, computer and computational scientists, engineers, software developers and practitioners, thus providing an ideal environment to share new ideas and important problems among specialists and users of optimization in academia, government, and industry.

This is the meeting of the SIAM Activity Group on Optimization.