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PDADS 2023 : The 3rd International Workshop on Parallel and Distributed Algorithms for Decision Sciences


When Aug 7, 2023 - Aug 7, 2023
Where Salt Lake City, Utah, USA
Submission Deadline Jun 3, 2023
Notification Due Jun 24, 2023
Final Version Due Jun 30, 2023
Categories    parallel computing   decision science   operations research   optimization

Call For Papers

The Third International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS-2023)
Date: August 7, 2023
Location: Salt Lake City, Utah, USA

PDADS will be co-hosted with the 52nd International Conference on Parallel Processing (ICPP 2023), August 7 - 10, 2023.

* Full Paper Submission Deadline: June 3, 2023
* Author Notification: June 24, 2023
* Camera-Ready Copy: June 30, 2023
* Workshop: August 7, 2023

PDADS 2023 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. Accepted papers will be published by the ACM International Conference Proceedings Series (ICPS), in conjunction with those of other ICPP workshops, in a volume entitled 52nd International Conference on Parallel Processing Workshops (ICPP 2022 Workshops). This volume will be available for download via the ACM Digital Library. For paper submission guidelines, visit:

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.
* Novel AI applications in systems and decision sciences on massively parallel computers.

Sudip K. Seal, Oak Ridge National Laboratory, USA
Meinolf Sellmann, InsideOpt, USA
Jim Ostrowski, University of Tennessee, USA
Yan Liu, Oak Ridge National Laboratory, USA

For additional queries, email: Yan Liu (yanliu[at]ornl[dot]gov) or Sudip Seal (sealsk[at]ornl[dot]gov)

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