Approximation and optimization: algorithms, complexity and applications
by Demetriou, Ioannis C. Pardalos, P. M., (Panos M.)
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 grad…
Read More ↓Title Statements
- Full title :Approximation and optimization : algorithms, complexity and applications / Ioannis C. Demetriou, Panos M. Pardalos, editors.
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 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.
Read More ↓Edition Statements
- Publication Date:2019
Publication Statements
- Publisher(s):Springer
- Place of Publication:Cham, Switzerland
- Language:eng
- Publication Date:2019
Notes
3.1.2 Generated Data
The Physical Object
- Description:1 online resource
Edition Identifiers
- ISBN:9783030127671, 3030127672, 9783030127688, 3030127680, 9783030127695, 3030127699
- DOI:10.1007/978-3-030-12767-1
- oclc:on1101771047
- LC Classification:QA221
- Dewey Decimal:511/.4
Table of 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: …
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