Nonlinear Combinatorial Optimization

  • Ding-Zhu Du
  • Panos M. Pardalos
  • Zhao Zhang

Part of the Springer Optimization and Its Applications book series (SOIA, volume 147)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Zhao Zhang, Xiaohui Huang
    Pages 1-35
  3. Zhao Zhang, Xiaohui Huang
    Pages 37-56
  4. Donglei Du, Qiaoming Han, Chenchen Wu
    Pages 57-79
  5. Weili Wu, Zhao Zhang, Ding-Zhu Du
    Pages 141-152
  6. Tiande Guo, Congying Han, Siqi Tang, Man Ding
    Pages 207-229
  7. Zaobo He, Yaguang Lin, Yi Liang, Xiaoming Wang, Akshita Maradapu Vera Venkata Sai, Zhipeng Cai
    Pages 231-250
  8. Smita Ghosh, Jianming Zhu, Weili Wu
    Pages 251-264
  9. Shuyang Gu, Hongwei Du, My T. Thai, Ding-Zhu Du
    Pages 265-272
  10. Yi Li, Ruidong Yan, Weili Wu
    Pages 273-284
  11. Shuyang Gu, Chuangen Gao, Weili Wu
    Pages 285-294
  12. Meghana N. Satpute, Luobing Dong, Weili Wu, Ding-Zhu Du
    Pages 295-308
  13. Jianxiong Guo, Weili Wu
    Pages 309-315

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.


discrete convex analysis discrete Newton methods primal-dual methods with convex relaxation submodular optimization optimization in data network designs spanning tree in wireless networks scheduling with energy allocation convex relaxation combinatorial optimization homogeneous sensor systems nonlinear function heterogeneous sensor systems nonlinear assignment problems Fractional Integer Progmanmming optimization in machine learning

Editors and affiliations

  1. 1.Department of Computer ScienceThe University of Texas at DallasRichardsonUSA
  2. 2.Department of Industrial & Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Department of Computer ScienceZhejiang Normal UniversityJinhuaChina

Bibliographic information