会議情報

PDADS 2022: International Workshop on Parallel and Distributed Algorithms for Decision Sciences

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投稿締切日:
2022-06-10 Extended
通知日:
2022-07-01
開催日:
2022-08-29
開催地:
Bordeaux, France
開催回数:
2
閲覧: 8353   フォロー: 0   参加: 0

論文募集

PDADS 2022 (International Workshop on Parallel and Distributed Algorithms for Decision Sciences) is an academic conference held in Bordeaux, France on 2022-08-29. The paper submission deadline is 2022-06-10 (extended). Acceptance notifications are sent on 2022-07-01.

Scope This workshop will focus on R&D efforts in cross-cutting areas at the intersection of algorithms research, computational sciences, decision sciences and optimization. Both regular papers as well as short position papers describing work-in-progress with innovative ideas related to the workshop topics are being solicited. **Select PDADS 2022 Workshop papers will be invited to extend the manuscripts to be considered for a Special Issue of the Journal of Parallel and Distributed Computing (JPDC) on Systems and Decision Sciences** Topics of Interest Topics of interest include, but are not limited to: 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. Optimization techniques in machine learning, such as high-performance first and higher order iterative optimization algorithms for minimizing loss and optimizing weight and bias tensors. Application-centric manuscripts involving optimizations for decision-making capabilities in systems such as logistics, transportation and urban planning, public health, manufacturing, energy (e.g., electric grids), digital twin systems (e.g., precision agriculture, smart cities, earth systems) operations management, finance and other areas are especially encouraged.
最終更新：Dou Sun（）

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