[hpc-announce] Call for papers: PDADS-2022 Second International Workshop on Parallel and Distributed Algorithms for Decision Sciences

[Liu, Yan]

Dear colleagues,

Please consider submit your research work to PDADS 2022. I apologize if you received duplicate messages.

Best regards,
Yan Liu, PhD
Publicity chair, PDADS 2022


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PDADS 2022 CALL FOR PAPERS
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The 2nd International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS)
Date: August 29, 2022
Location: Bordeaux, France
URL: https://www.csm.ornl.gov/workshops/PDADS2022/index.html
 
PDADS will be co-hosted with the 51st International Conference on Parallel Processing (ICPP 2022), August 29th to September 1st, 2022.
 
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IMPORTANT DATES
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* Paper Abstract Deadline: June 07, 2022 (AoE)
* Full Paper Submission Deadline: June 21, 2022 (AoE) (no extension)
* Author Notification: July 15, 2022 (AoE)
* Camera-Ready Deadline: August 15, 2022 (AoE)
* Workshop: August 29, 2022
 
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CALL FOR PAPERS
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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. The Second PDADS-2022 Workshop Proceedings will be published as a separate volume along with the ICPP 2022 conference proceedings. For paper submission guidelines, visit: https://www.csm.ornl.gov/workshops/PDADS2022/submission.html 

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TOPICS OF INTEREST
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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.
* 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.
 
For additional queries, email: Yan Liu <yanliu at ornl.gov> or Sudip Seal <sealsk at ornl.gov>
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