Buch, Englisch, Band 147, 315 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 653 g
Buch, Englisch, Band 147, 315 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 653 g
Reihe: Springer Optimization and Its Applications
ISBN: 978-3-030-16193-4
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Nichtlineare Wissenschaft
- Mathematik | Informatik Mathematik Topologie
- Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
- Mathematik | Informatik Mathematik Geometrie Algebraische Geometrie
Weitere Infos & Material
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 nonlinear objectives.- Solving combinatorial problems with machine learning methods.- Modeling malware propagation dynamics and developing prevention method in wireless sensor networks.- Composed influence in social networks.- Friending.- Optimization on content spread in social network studies.- Interation-aware influence maximization in social networks.- Multi-document extractive summarization as a nonlinear combinatorial optimization- Viral marketing for complementary products.
