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Approximation and Optimization

Algorithms, Complexity and Applications

Ioannis C. Demetriou (Hrsg.), Panos M. Pardalos (Hrsg.)

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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Analysis

Beschreibung

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.


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Schlagwörter

discrete noisy data, Riemann surfaces, portfolio selection, Data smoothing, spectroscopy, Algorithm design, non-linear programming, fuzzy control, binary sequences, monotonic approximation, networked control systems, nonlinear optimization, large scale optimization