This book focuses on the development of approximation-related algorithms and their relevant
applications. Individual contributions are written by leading experts and reflect emerging
directions and connections in data approximation and optimization. Chapters discuss state of
the art topics with highly relevant applications throughout science engineering technology
and social sciences. Academics researchers data science practitioners business analysts
social sciences investigators and graduate students will find the number of illustrations
applications and examples provided useful.This volume is based on the conference Approximation
and Optimization: Algorithms Complexity and Applications which was held in the National and
Kapodistrian University of Athens Greece June 29-30 2017. The mix of survey and research
content includes topics in approximations to discrete noisy data binary sequences design of
networks and energy systems fuzzy control large scale optimization noisy data
data-dependent approximation networked control systems machine learning optimal design no
free lunch theorem non-linearly constrained optimization spectroscopy.
