Parameterized Algorithms [electronic resource] / by Marek Cygan, Fedor V. Fomin, Łukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk, Saket Saurabh.
Material type: Computer filePublication details: Cham : Springer International Publishing : Imprint: Springer, 2015.Edition: 1st ed. 2015Description: XVII, 613 p. 84 illus., 25 illus. in color. online resourceISBN:- 9783319212753
- 518.1 23
Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | 518.1 (Browse shelf(Opens below)) | Available | EB1439 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online Close shelf browser (Hides shelf browser)
517.38 Sn21E Elements of Partial Differential Equations | 518 Numerical Partial Differential Equations: Finite Difference Methods | 518.02462 G959N Numerical Methods for Engineers | 518.1 Parameterized Algorithms | 518.1 Ab51A The Age of Algorithms | 518.1 Ed57H How to Think About Algorithms | 518.5 W676D The Design of Approximation Algorithms |
Introduction -- Kernelization -- Bounded Search Trees -- Iterative Compression -- Randomized Methods in Parameterized Algorithms -- Miscellaneous -- Treewidth -- Finding Cuts and Separators -- Advanced Kernelization Algorithms -- Algebraic Techniques: Sieves, Convolutions, and Polynomials -- Improving Dynamic Programming on Tree Decompositions -- Matroids -- Fixed-Parameter Intractability -- Lower Bounds Based on the Exponential-Time Hypothesis -- Lower Bounds for Kernelization.
This comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way. The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds. All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.
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