Du D.-Z., Pardalos P.M., Zhang Z. (Eds.) Nonlinear Combinatorial Optimization

Springer, 2019. — 317 p. — (Springer Optimization and Its Applications 147). — ISBN: 978-3-030-16193-4.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.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