Introduction:
IEEE PDCO 2017, Orlando USA, is the result of the merge of the IEEE Parallel Computing and Optimization (PCO) workshop and the IEEE Nature Inspired Distributed Computing (NIDISC) workshop that have been held in conjunction with the IEEE International Parallel and Distributed Processing Symposium for the past years.

Scope:
The IEEE Workshop on Parallel / Distributed Computing and Optimization aims at providing a forum for scientific researchers and engineers on recent advances in the field of parallel or distributed computing for difficult optimization problems, ranging from theoretical to applied problems.
The latter include 0-1 multidimensional knapsack problems and cutting stock problems, large scale linear programming problems, nonlinear optimization problems and global optimization problems. Emphasis will be placed on new techniques for solving these difficult problems, like cooperative methods for integer programming problems, nature-inspired techniques and hybrid methods. Aspects related to Combinatorial Scientific Computing (CSC) will also be treated. We also solicit submissions of original manuscripts on sparse matrix computations and related topics (including graph algorithms); and related methods and tools for their efficiency on different parallel systems. The use of new approaches in parallel and distributed computing like GPU, MIC, cloud computing, volunteer computing will be considered. Applications combining traditional parallel and distributed computing and optimization techniques as well as theoretical issues (convergence, complexity, etc.) are welcome. Application domains of interest include (but are not limited to) cloud computing, planning, logistics, manufacturing, finance, telecommunications and computational biology.

Topics:
* Integer programming, linear programming, nonlinear programming;
* Global optimization, polynomial optimization;
* Exact methods, heuristics, metaheuristics, hybrid methods;
* Cooperative methods, hybrid methods;
* Parallel / distributed algorithms for combinatorial optimization;
* Parallel / distributed  metaheuristics;
* Distributed optimization algorithms;
* Nature inspired distributed computing;
* Parallel sparse matrix computations, graph algorithms, load balancing;
* Peer to peer computing and optimization problems;
* Applications: cloud computing, planning, logistics, manufacturing, finance, telecommunications, computational biology, combinatorial algorithms in high performance computing.