PDADS 2022 : PDADS-2022 Second International Workshop on Parallel and Distributed Algorithms for Decision Sciences
Call For Papers
PDADS 2022 CALL FOR PAPERS
The 2nd International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS)
Date: August 29, 2022
Location: Bordeaux, France
PDADS will be co-hosted with the 51st International Conference on Parallel Processing (ICPP 2022), August 29th to September 1st, 2022.
* Paper Abstract Deadline: June 01, 2022 (AoE)
* Full Paper Submission Deadline: June 10, 2022 (AoE) (no extension)
* Author Notification: July 1, 2022 (AoE)
* Camera-Ready Deadline: July 11, 2022 (AoE)
* Workshop: August 29, 2022
CALL FOR PAPERS
PDADS 2022 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 51st 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: https://www.csm.ornl.gov/workshops/PDADS2022/submission.html
** Arrangements are ongoing for a Special Issue of a premier parallel and distributed computing journal where selected papers accepted in the workshop will be invited for submission of extended versions. Look out for updates in the Announcements section of the workshop URL **
TOPICS OF INTEREST
* 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.
Sudip K. Seal, Oak Ridge National Laboratory, USA
Meinolf Sellmann, Shopify, 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)