Broadens understanding of nonlinear combinatorial optimization applications to machine learning, social computing, cloud computing, wireless communication, and data science
Features articles by leading experts in nonlinear combinatorial optimization
Outlines theoretical developments which utilize Newton methods submodular optimization, and non-submodular maximization
Broadens understanding of nonlinear combinatorial optimization applications to machine learning, social computing, cloud computing, wireless communication, and data science
Features articles by leading experts in nonlinear combinatorial optimization
Outlines theoretical developments which utilize Newton methods submodular optimization, and non-submodular maximization
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 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.
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.
"Each chapter can be read by its own and does not assume knowledge from one of the other chapters. ... All in all, the book `Nonlinear combinatorial optimization´ introduces some interesting topics in this relatively new field.” (Isabel Beckenbach, zbMATH 1480.90209, 2022)
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.
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 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.
"Each chapter can be read by its own and does not assume knowledge from one of the other chapters. ... All in all, the book 'Nonlinear combinatorial optimization' introduces some interesting topics in this relatively new field." (Isabel Beckenbach, zbMATH 1480.90209, 2022)
Über den Autor
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Inhaltsverzeichnis
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.
Klappentext
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.
Broadens understanding of nonlinear combinatorial optimization applications to machine learning, social computing, cloud computing, wireless communication, and data science
Features articles by leading experts in nonlinear combinatorial optimization
Outlines theoretical developments which utilize Newton methods submodular optimization, and non-submodular maximization
