Meetings/Workshops on Operational research in the United States (USA)

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IPCO XXII — 22nd Conference on Integer Programming and Combinatorial Optimization
19 May 2021 - 21 May 2021 • Atlanta, GA, United States
H. Milton Stewart School of Industrial and Systems Engineering
The IPCO conference is under the auspices of the Mathematical Optimization Society and is held every year. The conference is a forum for researchers and practitioners working on various aspects of integer programming and combinatorial optimization. The aim is to present recent developments in theory, computation, and applications in these areas.

The conference will be preceded by a Summer school (May 17-18)

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OP21 — SIAM Conference on Optimization
20 Jul 2021 - 23 Jul 2021 • Washington, United States
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.
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PDADS — The 1st International Workshop on Parallel and Distributed Algorithms for Decision Sciences
09 Aug 2021 • Chicago, United States
Co-hosted with the 50th International Conference on Parallel Processing (ICPP 2021)
Welcome to the First International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS), 2021. The goal of this workshop is to create a forum for scientists, engineers and practitioners to share new results and collaborate on ideas at the intersection of parallel and distributed algorithms, decision sciences and combinatorial optimization. There is a growing recognition that a variety of science and engineering use-cases can benefit from parallel and distributed optimization frameworks that can deliver real-time decision-making capabilities. Especially in this age of ever-increasing powerful computing technologies, optimization-driven decision science problems that were otherwise considered computationally prohibitive for integration within scientific and engineering applications, especially those needing real-time decision-making capabilities, are now becoming possible. This workshop will focus on the theory, design, development and deployment of parallel and distributed algorithms for faster and more accurate solutions of different types of optimization problems that arise within the broader purview of the decision sciences.
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Parallel algorithms for integer/mixed-integer programming, linear/nonlinear programming, stochastic programming, robust optimization, combinatorial optimization, feasibility problems (SAT, CP, etc.), Parallel heuristic and meta-heuristic algorithms, Parallel evolutionary algorithms, swarm intelligence, ant colonies, other, Parallel local and complete search methods, Learning approaches for optimization in parallel and distributed environments, Parallel and distributed approaches for parameter tuning, simulation-based optimization, and black box optimization, Parallel algorithm portfolios, Quantum optimization algorithms, Use of randomization techniques for scalable decision support systems, Application of decision support systems on novel computing platforms (shared/distributed memory, edge devices, cloud platforms, field programable gate arrays, quantum computers, etc.), Use of parallel computing for timely and/or higher quality decision support * Theoretical analysis of convergence and/or complexity of parallel optimization algorithms and decision support systems.
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Last updated: 10 May 2021