| ISBN13: | 9783031783685 |
| ISBN10: | 3031783689 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 219 oldal |
| Méret: | 235x155 mm |
| Nyelv: | angol |
| Illusztrációk: | 10 Illustrations, black & white; 54 Illustrations, color |
| 700 |
Optimization, Discrete Mathematics and Applications to Data Sciences
EUR 129.99
Kattintson ide a feliratkozáshoz
This book delves into the dynamic intersection of optimization and discrete mathematics, offering a comprehensive exploration of their applications in data sciences. Through a collection of high-quality papers, readers will gain insights into cutting-edge research and methodologies that address complex problems across a wide array of topics.
The chapters cover an impressive range of subjects, including advances in the study of polynomials, combinatorial identities, and global optimization algorithms. Readers will encounter innovative approaches to predictive models for non-performing loans, rainbow greedy matching algorithms, and the cost of detection in interaction testing. The book also examines critical issues such as demand aggregation, mid-term energy planning, and minimum-cost energy flow. Contributions from expert authors provide a deep dive into multilevel low-rank matrices, the protection of medical image authenticity, and the mathematical intricacies of the Braess paradox. This volume invites readers to explore diverse perspectives and theoretical insights that are both practical and forward-thinking.
This publication is an invaluable resource for graduate students and advanced researchers in the fields of optimization and discrete mathematics. It is particularly beneficial for those interested in their applications within data sciences. Academics across these disciplines will find the book's content relevant to their work, while practitioners seeking to apply these concepts in industry will appreciate its practical case studies. Whether you are a scholar or a professional, this book offers a wealth of knowledge that bridges theory with real-world applications.
This book delves into the dynamic intersection of optimization and discrete mathematics, offering a comprehensive exploration of their applications in data sciences. Through a collection of high-quality papers, readers will gain insights into cutting-edge research and methodologies that address complex problems across a wide array of topics.
The chapters cover an impressive range of subjects, including advances in the study of polynomials, combinatorial identities, and global optimization algorithms. Readers will encounter innovative approaches to predictive models for non-performing loans, rainbow greedy matching algorithms, and the cost of detection in interaction testing. The book also examines critical issues such as demand aggregation, mid-term energy planning, and minimum-cost energy flow. Contributions from expert authors provide a deep dive into multilevel low-rank matrices, the protection of medical image authenticity, and the mathematical intricacies of the Braess paradox. This volume invites readers to explore diverse perspectives and theoretical insights that are both practical and forward-thinking.
This publication is an invaluable resource for graduate students and advanced researchers in the fields of optimization and discrete mathematics. It is particularly beneficial for those interested in their applications within data sciences. Academics across these disciplines will find the book's content relevant to their work, while practitioners seeking to apply these concepts in industry will appreciate its practical case studies. Whether you are a scholar or a professional, this book offers a wealth of knowledge that bridges theory with real-world applications.
On the morphism 1 ? 121, 2 ? 12221.- Polynomials and combinatorial identities.- Rainbow Greedy Matching Algorithms.- Predictive models of Non-Performing Loans: the case of Greece.- The Cost of Detection in Interaction Testing.- On the study of cycle chains representing non-reversible Markov chains associated with random walks with jumps in fixed environments.- Applying Distance Measures for Discrete Data.- Demand aggregation and mid-term energy planning problem on the business layer.- Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices.- A Code-based Watermarking Scheme for the Protection of Authenticity of Medical Images.- The minimum cost energy flow problem under demand uncertainty Effect on optimal solution, variability, worst and best case scenarios.- A mathematical study of the Braess?s Paradox within a network comprising four nodes, five edges, and linear time functions.- On similiarities between two global optimization algorithms based on different (Bayesian and Lipschitzian) approaches.
