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Approximation and Optimization

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Cover of 'Approximation and Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Evaluation Complexity Bounds for Smooth Constrained Nonlinear Optimization Using Scaled KKT Conditions and High-Order Models
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    Chapter 3 Data-Dependent Approximation in Social Computing
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    Chapter 4 Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey
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    Chapter 5 No Free Lunch Theorem: A Review
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    Chapter 6 Piecewise Convex–Concave Approximation in the Minimax Norm
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    Chapter 7 A Decomposition Theorem for the Least Squares Piecewise Monotonic Data Approximation Problem
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    Chapter 8 Recent Progress in Optimization of Multiband Electrical Filters
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    Chapter 9 Impact of Error in Parameter Estimations on Large Scale Portfolio Optimization
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    Chapter 10 Optimal Design of Smart Composites
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    Chapter 11 Tax Evasion as an Optimal Solution to a Partially Observable Markov Decision Process
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Title
Approximation and Optimization
Published by
Springer International Publishing, June 2019
DOI 10.1007/978-3-030-12767-1
ISBNs
978-3-03-012766-4, 978-3-03-012767-1
Editors

Demetriou, Ioannis C., Pardalos, Panos M.

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