Exact Exponential Algorithms

• Κύριοι συγγραφείς: Fomin, Fedor V. (Συγγραφέας), Kratsch, Dieter (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Σειρά: Texts in Theoretical Computer Science. An EATCS Series,
• Θέματα: Mathematics.; Computer programming.; Algorithms.; Mathematical optimization.; Discrete mathematics.; Combinatorics.; Discrete Mathematics.; Optimization.; Programming Techniques.; Algorithm Analysis and Problem Complexity.

 Today most computer scientists believe that NP-hard problems cannot be solved by polynomial-time algorithms. From the polynomial-time perspective, all NP-complete problems are equivalent but their exponential-time properties vary widely. Why do some NP-hard problems appear to be easier than others? Are there algorithmic techniques for solving hard problems that are significantly faster than the exhaustive, brute-force methods? The algorithms that address these questions are known as exact exponential algorithms. The history of exact exponential algorithms for NP-hard problems dates back to the 1960s. The two classical examples are Bellman, Held and Karp’s dynamic programming algorithm for the traveling salesman problem and Ryser’s inclusion–exclusion formula for the permanent of a matrix. The design and analysis of exact algorithms leads to a better understanding of hard problems and initiates interesting new combinatorial and algorithmic challenges. The last decade has witnessed a rapid development of the area, with many new algorithmic techniques discovered. This has transformed  exact algorithms into a very active research field. This book provides an introduction to the area and explains the most common algorithmic techniques, and the text is supported throughout with exercises and detailed notes for further reading. The book is intended for advanced students and researchers in computer science, operations research, optimization and combinatorics.  . 

 Branching -- Dynamic Programming -- Inclusion-Exclusion -- Treewidth -- Measure & Conquer -- Subset Convolution -- Local Search and SAT -- Split and List -- Time Versus Space -- Miscellaneous -- Conclusions, Open Problems and Further Directions.