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Approximation and optimization : algorithms, complexity and applications

Author: Ioannis C Demetriou; P M Pardalos
Publisher: Cham, Switzerland : Springer, [2019]
Series: Springer optimization and its applications, v. 145.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Additional Physical Format: Print version:
Demetriou, Ioannis C.
Approximation and Optimization : Algorithms, Complexity and Applications.
Cham : Springer, ©2019
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ioannis C Demetriou; P M Pardalos
ISBN: 9783030127671 3030127672 9783030127688 3030127680 9783030127695 3030127699
OCLC Number: 1101771047
Notes: 3.1.2 Generated Data
Description: 1 online resource
Contents: Intro; Preface; Contents; Contributors; Introduction; 1 Survey; Evaluation Complexity Bounds for Smooth Constrained Nonlinear Optimization Using Scaled KKT Conditions and High-Order Models; 1 Introduction; 2 Convex Constraints; 3 The General Constrained Case; 4 Discussion; References; Data-Dependent Approximation in Social Computing; 1 Introduction; 2 Example; 3 Theoretical Notes; 4 Conclusion; References; Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey; 1 Introduction; 2 Basic Concepts of Multi-Objective Optimization; 3 Data Preprocessing 4 Supervised Learning5 Unsupervised Learning; 6 A Few of the Most Recent Applications; 7 Synopsis and Discussion; References; No Free Lunch Theorem: A Review; 1 Introduction; 2 Early Developments; 3 No Free Lunch for Optimization and Search; 4 More Recent Work of Wolpert; 5 NFL for Optimization and Evolutionary Algorithms; 5.1 No Free Lunches and Evolutionary Algorithms; 5.2 No Free Lunches and Meta-Heuristic Techniques; 6 NFL for Supervised Learning; 6.1 No Free Lunch for Early Stopping; 6.2 No Free Lunch for Cross-Validation 6.3 Real-World Machine Learning Classification and No Free Lunch Theorems: An Experimental Approach7 Synopsis and Concluding Remarks; References; Piecewise Convex-Concave Approximation in the Minimax Norm; 1 Introduction; 2 The Algorithm; 2.1 The Case q=0; 2.2 The Case q=1; 2.3 The Case q=2; 2.4 The General Case; 3 Numerical Results and Conclusions; 3.1 Synthetic Test Data; 3.2 Real Test Data; 3.3 Conclusion; References; A Decomposition Theorem for the Least Squares Piecewise Monotonic Data Approximation Problem; 1 Introduction; 2 The Theorem; 3 Estimation of Peaks of an NMR Spectrum 4 SummaryReferences; Recent Progress in Optimization of Multiband Electrical Filters; 1 History and Background; 2 Optimization Problem for Multiband Filter; 2.1 Four Settings; 2.1.1 Minimal Deviation; 2.1.2 Minimal Modified Deviation; 2.1.3 Third Zolotarëv Problem; 2.1.4 Fourth Zolotarëv Problem; 2.2 Study of Optimization Problem; 3 Zolotarëv Fraction; 4 Projective View; 4.1 Projective Problem Setting; 4.2 Decomposition into Subclasses; 4.3 Extremal Problem for Classes; 4.4 Equiripple Property; 5 Problem Genesis: Signal Processing; 6 Approaches to Optimization; 6.1 Remez-Type Methods 6.2 Composite Filters6.3 Ansatz Method; 7 Novel Analytical Approach; 8 Examples of Filter Design; References; Impact of Error in Parameter Estimations on Large Scale Portfolio Optimization; 1 Introduction; 2 Theoretical Background; 2.1 Portfolio Optimization; 2.1.1 Markowitz Model and Its Variations; 2.1.2 Single-Factor Model; 2.1.3 Multi-Factor Model; 2.2 Parameters Estimation; 2.2.1 Estimation of Means; 2.2.2 Estimation of Covariances; 2.2.3 Ledoit and Wolf Shrinkage Estimator for Covariance Matrix; 3 Properties of Selected Portfolios; 3.1 Risk of Selected Portfolios; 3.1.1 Real Data
Series Title: Springer optimization and its applications, v. 145.
Responsibility: Ioannis C. Demetriou, Panos M. Pardalos, editors.

Abstract:

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.

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