Kernelization: theory of parameterized preprocessing
Preprocessing, or data reduction, is a standard technique for simplifying and speeding up computation. Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors provide an overview of basic methods and impo...
Gespeichert in:
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| Weitere beteiligte Personen: | , , |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2019
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| Links: | https://doi.org/10.1017/9781107415157 |
| Zusammenfassung: | Preprocessing, or data reduction, is a standard technique for simplifying and speeding up computation. Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors provide an overview of basic methods and important results, with accessible explanations of the most recent advances in the area, such as meta-kernelization, representative sets, polynomial lower bounds, and lossy kernelization. The text is divided into four parts, which cover the different theoretical aspects of the area: upper bounds, meta-theorems, lower bounds, and beyond kernelization. The methods are demonstrated through extensive examples using a single data set. Written to be self-contained, the book only requires a basic background in algorithmics and will be of use to professionals, researchers and graduate students in theoretical computer science, optimization, combinatorics, and related fields. |
| Umfang: | 1 Online-Ressource (xiv, 515 Seiten) |
| ISBN: | 9781107415157 |
Internformat
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| 100 | 1 | |a Fomin, Fedor V. | |
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| 520 | |a Preprocessing, or data reduction, is a standard technique for simplifying and speeding up computation. Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors provide an overview of basic methods and important results, with accessible explanations of the most recent advances in the area, such as meta-kernelization, representative sets, polynomial lower bounds, and lossy kernelization. The text is divided into four parts, which cover the different theoretical aspects of the area: upper bounds, meta-theorems, lower bounds, and beyond kernelization. The methods are demonstrated through extensive examples using a single data set. Written to be self-contained, the book only requires a basic background in algorithmics and will be of use to professionals, researchers and graduate students in theoretical computer science, optimization, combinatorics, and related fields. | ||
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| spelling | Fomin, Fedor V. Kernelization theory of parameterized preprocessing Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi Cambridge Cambridge University Press 2019 1 Online-Ressource (xiv, 515 Seiten) txt c cr Preprocessing, or data reduction, is a standard technique for simplifying and speeding up computation. Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors provide an overview of basic methods and important results, with accessible explanations of the most recent advances in the area, such as meta-kernelization, representative sets, polynomial lower bounds, and lossy kernelization. The text is divided into four parts, which cover the different theoretical aspects of the area: upper bounds, meta-theorems, lower bounds, and beyond kernelization. The methods are demonstrated through extensive examples using a single data set. Written to be self-contained, the book only requires a basic background in algorithmics and will be of use to professionals, researchers and graduate students in theoretical computer science, optimization, combinatorics, and related fields. Lokshtanov, Daniel 1984- Saurabh, Saket Zehavi, Meirav Erscheint auch als Druck-Ausgabe 9781107057760 |
| spellingShingle | Fomin, Fedor V. Kernelization theory of parameterized preprocessing |
| title | Kernelization theory of parameterized preprocessing |
| title_auth | Kernelization theory of parameterized preprocessing |
| title_exact_search | Kernelization theory of parameterized preprocessing |
| title_full | Kernelization theory of parameterized preprocessing Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi |
| title_fullStr | Kernelization theory of parameterized preprocessing Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi |
| title_full_unstemmed | Kernelization theory of parameterized preprocessing Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi |
| title_short | Kernelization |
| title_sort | kernelization theory of parameterized preprocessing |
| title_sub | theory of parameterized preprocessing |
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