*** Abstracts for SHORT position papers and REGULAR paper solicited for PDADS 2021. For more information, read below. ***

* Paper Abstract Deadline: May 24, 2021 (AoE)
* Full Paper Submission Deadline: May 31, 2021 (AoE)
* Author Notification: June 21, 2021 (AoE)
* Camera-Ready Deadline: June 28, 2021 (AoE)
* Workshop: August 9, 2021

PDADS 2021
The 1st International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS)
Date: August 9, 2021, Chicago, USA
URL: https://www.csm.ornl.gov/workshops/PDADS/index.html
PDADS will be co-hosted with the 50th International Conference on Parallel Processing (ICPP 2021), August 9-12, 2021.

PDADS 2021 will focus on research at the intersection of parallel and distributed algorithms, decision sciences and combinatorial optimization. The workshop will discuss latest trends and identify technology gaps in high-performance decision sciences and combinatorial optimization technologies for extant and next-generation scientific, engineering and other applications. The workshop adopts an inclusive definition of the sciences that includes the social sciences, behavioral sciences or others.

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 ACM ICPS in a workshop proceedings volume available for download from the ACM digital library. For paper submission guidelines, visit: https://www.csm.ornl.gov/workshops/PDADS/submission.html

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.

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