Non-Convex Multi-Objective Optimization

Panos M. Pardalos (), Antanas Žilinskas () and Julius Žilinskas ()
Additional contact information
Panos M. Pardalos: University of Florida
Antanas Žilinskas: Vilnius University
Julius Žilinskas: Vilnius University

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2017
ISBN: 978-3-319-61007-8
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Chapters in this book:

Ch Chapter 1 Definitions and Examples

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 10 Multi-Objective Optimization Aided Visualization of Business Process Diagrams

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 2 Scalarization

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 3 Approximation and Complexity

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 4 A Brief Review of Non-convex Single-Objective Optimization

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 5 Multi-Objective Branch and Bound

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 6 Worst-Case Optimal Algorithms

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 7 Statistical Models Based Algorithms

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 8 Probabilistic Bounds in Multi-Objective Optimization

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

Ch Chapter 9 Visualization of a Set of Pareto Optimal Decisions

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas

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DOI: 10.1007/978-3-319-61007-8

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