Advertisement

Approximation and Optimization

Algorithms, Complexity and Applications

  • Ioannis C. Demetriou
  • Panos M. Pardalos
Book

Part of the Springer Optimization and Its Applications book series (SOIA, volume 145)

Table of contents

  1. Front Matter
    Pages i-x
  2. Ioannis C. Demetriou, Panos M. Pardalos
    Pages 1-4
  3. Weili Wu, Yi Li, Panos M. Pardalos, Ding-Zhu Du
    Pages 27-34
  4. Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, Michael N. Vrahatis
    Pages 35-55
  5. Stavros P. Adam, Stamatios-Aggelos N. Alexandropoulos, Panos M. Pardalos, Michael N. Vrahatis
    Pages 57-82
  6. Valery A. Kalyagin, Sergey V. Slashchinin
    Pages 151-184
  7. Georgios K. Tairidis, Georgia Foutsitzi, Georgios E. Stavroulakis
    Pages 185-217
  8. Paraskevi Papadopoulou, Dimitrios Hristu-Varsakelis
    Pages 219-237

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  1. 1.Department of EconomicsUniversity of AthensAthensGreece
  2. 2.Department of Industrial & Systems EngineeringUniversity of FloridaGainesvilleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-12767-1
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-12766-4
  • Online ISBN 978-3-030-12767-1
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • Buy this book on publisher's site