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Open Problems in Optimization and Data Analysis

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Cover of 'Open Problems in Optimization and Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 A Note on Open Problems and Challenges in Optimization Theory and Algorithms
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    Chapter 2 Social Influence-Based Optimization Problems
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    Chapter 3 New Statistical Robust Estimators, Open Problems
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    Chapter 4 Optimal Location Problems for Electric Vehicles Charging Stations: Models and Challenges
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    Chapter 5 Supply and Demand Selection Problems in Supply Chain Planning
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    Chapter 6 Open Problems in Green Supply Chain Modeling and Optimization with Carbon Emission Targets
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    Chapter 7 Variants and Formulations of the Vehicle Routing Problem
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    Chapter 8 New MIP model for Multiprocessor Scheduling Problem with Communication Delays
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    Chapter 9 On Optimization Problems in Urban Transport
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    Chapter 10 Some Aspects of the Stackelberg Leader/Follower Model
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    Chapter 11 Open Research Areas in Distance Geometry
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    Chapter 12 A Circle Packing Problem and Its Connection to Malfatti’s Problem
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    Chapter 13 Review of Basic Local Searches for Solving the Minimum Sum-of-Squares Clustering Problem
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    Chapter 14 On the Design of Metaheuristics-Based Algorithm Portfolios
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    Chapter 15 Integral Simplex Methods for the Set Partitioning Problem: Globalisation and Anti-Cycling
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    Chapter 16 Open Problems on Benders Decomposition Algorithm
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    Chapter 17 An Example of Nondecomposition in Data Fitting by Piecewise Monotonic Divided Differences of Order Higher Than Two
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Title
Open Problems in Optimization and Data Analysis
Published by
Springer International Publishing, October 2018
DOI 10.1007/978-3-319-99142-9
ISBNs
978-3-31-999141-2, 978-3-31-999142-9
Editors

Pardalos, Panos M., Migdalas, Athanasios

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 110 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 15%
Researcher 9 8%
Student > Doctoral Student 9 8%
Student > Master 7 6%
Professor 7 6%
Other 19 17%
Unknown 43 39%
Readers by discipline Count As %
Engineering 17 15%
Computer Science 10 9%
Mathematics 9 8%
Social Sciences 5 5%
Economics, Econometrics and Finance 4 4%
Other 20 18%
Unknown 45 41%