[Iefac.list] Fwd: Submit Paper Abstracts by May 24, 2021 to the First International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS 2021)

[Xueping Li]

Apologies for cross-posting.

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


================

IMPORTANT DATES

================

* 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



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PDADS 2021

==========

The 1st International Workshop on Parallel and Distributed Algorithms for
Decision Sciences (PDADS)

Date: August 9, 2021, Chicago, USA

URL: 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.



===============

CALL FOR PAPERS

===============

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:
www.csm.ornl.gov/workshops/PDADS/submission.html



=================

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.



For additional queries, email: pdads-chairs at email.ornl.gov

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