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Graph minor theory is one of the most influential and well-developed areas of graph theory, yet its key results, particularly the work of Robertson and Seymour, have remained scattered across numerous technical papers. This book fills an important gap by providing a comprehensive, structured treatment of the subject. Divided into three main parts, the book first introduces the fundamentals of graph minor theory, focusing on the deep and powerful Minor Structure Theorem. It offers a clear roadmap for understanding the theorem’s proof, presenting its key ingredients while omitting only the most technical details. The second part explores a variety of applications, from algorithmic results to connections with the Linear Hadwiger Conjecture and graph coloring problems. The final section presents alternative approaches to graph minor theory that do not rely on the Minor Structure Theorem, covering topics such as sublinear separators, density, and isomorphism testing. The exposition is rigorous yet accessible, striving to balance depth with readability. While some parts remain dense due to the complexity of the subject, the author provides valuable insights and explanations that make challenging concepts more approachable. The book not only serves as an excellent learning resource for graduate students and researchers entering the field but also as a long-lasting reference for experts.
Del 8934 - Lecture Notes in Computer Science
Mathematical and Engineering Methods in Computer Science
9th International Doctoral Workshop, MEMICS 2014, Telč, Czech Republic, October 17--19, 2014, Revised Selected Papers
Häftad, Engelska, 2015
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This volume contains the post-proceedings of the 9th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science, MEMICS 2014, held in Telč, Czech Republic, in October 2014.The 13 thoroughly revised papers were carefully selected out of 28 submissions and are presented together with 4 invited papers. The topics covered by the papers include: algorithms, logic, and games; high performance computing; computer aided analysis, verification, and testing; hardware design and diagnostics; computer graphics and image processing; and artificial intelligence and natural language processing.