Approximation and Optimization

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.

• 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

“This book would be suitable as a textbook at any level, but it could be of interest to researchers currently working on optimization problems.” (MAA Reviews, February 24, 2020)

• Department of Economics, University of Athens, Athens, Greece

  Ioannis C. Demetriou

• Department of Industrial & Systems Engineering, University of Florida, Gainesville, USA

  Panos M. Pardalos

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