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Approximation and Optimization: Algorithms, Complexity and Applications
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Catalogue Information
Field name
Details
Dewey Class
511.4
Title
Approximation and Optimization ([EBook]) : Algorithms, Complexity and Applications / edited by Ioannis C. Demetriou, Panos M. Pardalos.
Added Personal Name
Demetriou, Ioannis C.
Pardalos, Panos M.
Other name(s)
SpringerLink (Online service)
Edition statement
1st ed. 2019.
Publication
Cham : Springer International Publishing , 2019.
Physical Details
X, 237 pages: 56 illus., 27 illus. in color. : online resource.
Series
Springer Optimization and Its Applications
; 145
ISBN
9783030127671
Summary Note
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.:
Contents note
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.
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site
https://doi.org/10.1007/978-3-030-12767-1
Links to Related Works
Subject References:
Algorithms
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Approximation theory
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Approximations and Expansions
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Calculus of variations
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Calculus of variations and optimal control; optimization
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Numerical Analysis
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Probabilities
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Probability theory and stochastic processes
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Authors:
Demetriou, Ioannis C.
.
editor
.
Pardalos, Panos M.
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Corporate Authors:
SpringerLink (Online service)
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Series:
Springer Optimization and Its Applications
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Classification:
511.4
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511.4 (DDC 23)
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