by Ding-Zhu Du (Editor), Panos M. Pardalos (Editor), Zhao Zhang (Editor)
About the Author
Ding-Zhu Du is the EiC of the Journal of Combinatorial Optimization (Springer). Prof. Du is co-editor of the Handbook of Combinatorial Optimization. He was also co-author of "Mathematical Theory of Optimization", "Connected Dominating Set: Theory and Applications", and "Introduction to Combinatorial Optimization".
Panos M. Pardalos serves as Distinguished Professor of Industrial and Systems Engineering at the University of Florida, as well as the Director of the Center for Applied Optimization. He holds a PhD in Computer and Information Sciences from the University of Minnesota, Minneapolis, and has published nearly twenty books and over four hundred papers.
About this book
Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.
Brief contents
- A Role of Minimum Spanning Tree
- Discrete Newton Method
- An Overview of Submodular Optimization: Single- and Multi-Objectives
- Discrete Convex Optimization and Applications in Supply Chain Management
- Thresholding Methods for Streaming Submodular Maximization with a Cardinality Constraint and Its Variants
- Nonsubmodular Optimization
- On Block-Structured Integer Programming and Its Applications
- Online Combinatorial Optimization Problems with Non-linear Objectives
- Solving Combinatorial Problems with Machine Learning Methods
- Modeling Malware Propagation Dynamics and Developing Prevention Methods in Wireless Sensor Networks
- Composed Influence Maximization in Social Networks
- Friending
- Optimization on Content Spread in Social Network Studies
- Interaction-Aware Influence Maximization in Social Networks
- Multi-Document Extractive Summarization as a Non-linear Combinatorial Optimization Problem
- Viral Marketing for Complementary Products
Series: Springer Optimization and Its Applications (Book 147)
Pages: 392 pages
Publisher: Springer; 1st ed. 2019 edition (July 29, 2019)
Language: English
ISBN-10: 3030161935
ISBN-13: 978-3030161934
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