Introduction:

PDCO 2017, Orlando USA, is the seventh edition of the IEEE Workshop on Parallel / Distributed Computing and Optimization that is held in conjunction with the 31th IEEE International Parallel and Distributed Processing Symposium. The previous editions were held in Anchorage USA 2011, Shanghai China 2012, Boston USA 2013, Phoenix USA 2014, Hyderabad India 2015, and Chicago USA 2016.

**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 combinatorial optimization problems, like 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 the solution of these difficult problems like cooperative methods for integer programming problems and hybridization techniques. Aspects related to Combinatorial Scientific Computing (CSC) will also be treated. In particular, we solicit submissions of original manuscripts on sparse matrix computations and relatives (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. Application to cloud computing, planning, logistics, manufacturing, finance, telecommunications and computational biology will be considered.

**Topics:**

* Integer programming, linear programming, nonlinear programming;

* Global optimization, polynomial optimization;

* Exact methods, heuristics, metaheuristics;

* 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.