Nonlinear Combinatorial Optimization
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Beschreibung
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.
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
Autor*in
Ding-Zhu Du
Themen in »Nonlinear Combinatorial Optimization«
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
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“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)
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