Approximation and Optimization: Algorithms, Complexity and Applications: Springer Optimization and Its Applications, cartea 145
Editat de Ioannis C. Demetriou, Panos M. Pardalosen Limba Engleză Paperback – 14 aug 2020
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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Specificații
ISBN-13: 9783030127695
ISBN-10: 3030127699
Pagini: 237
Ilustrații: X, 237 p. 56 illus., 27 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.35 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria Springer Optimization and Its Applications
Locul publicării:Cham, Switzerland
ISBN-10: 3030127699
Pagini: 237
Ilustrații: X, 237 p. 56 illus., 27 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.35 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria Springer Optimization and Its Applications
Locul publicării:Cham, Switzerland
Cuprins
Introduction.- Evaluation Complexity Bounds for Smooth Constrained Nonlinear Optimization using Scaled KKT Conditions and High-order Models.- Data-Dependent Approximation in Social Computing.- Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: a Recent Survey.- No Free Lunch Theorem, a Review.- Piecewise Convex-Concave Approximation in the Minimax Norm.- A Decomposition Theorem for the Least Squares Piecewise Monotonic Data Approximation Problem.- Recent Progress in Optimization of Multiband Electrical Filters.- Impact of Error in Parameter Estimations on Large Scale Portfolio Optimization.- Optimal Design of Smart Composites.- Tax Evasion as an Optimal Solution to a Partially Observable Markov Decision Process.
Recenzii
“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)
Textul de pe ultima copertă
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; fuzzycontrol; large scale optimization; noisy data; data-dependent approximation; networked control systems; machine learning ; optimal design; no free lunch theorem; non-linearly constrained optimization; spectroscopy.
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; fuzzycontrol; large scale optimization; noisy data; data-dependent approximation; networked control systems; machine learning ; optimal design; no free lunch theorem; non-linearly constrained optimization; spectroscopy.
Caracteristici
Presents approximation-related algorithms and their relevant applications Contains new approaches and techniques to data-dependent approximation Highlights new research results