Algorithm Portfolios and Teams in Parallel Optimization

Volodymyr P. Shylo () and Oleg V. Shylo ()
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Volodymyr P. Shylo: V.M. Glushkov Institute of Cybernetics
Oleg V. Shylo: University of Tennessee

A chapter in Optimization Methods and Applications, 2017, pp 481-493 from Springer

Abstract: Abstract Parallel computing systems are readily available to optimization experts in industry and academia, providing tools for solving optimization models of unprecedented scale. Unfortunately, there is no simple way to adapt existing optimization theory and algorithms to fully realize the distributed power of these systems, preventing their widespread usage. At the same time, optimization models that fully capture the essence of industrial scale problems, such as stochastic conditions and outcomes, multi-stage structure, and multi-objective criteria, require capacities only afforded by parallel computing. Without efficient and scalable parallel methods we are unable to utilize these computational resources. This chapter outlines these challenges and illustrates theoretical extensions to deal with such limitations.

Date: 2017
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DOI: 10.1007/978-3-319-68640-0_23

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