Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press CUP, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 515 pages. 9.50x6.25x1.25 inches. In Stock.
Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
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Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 98,66
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - A complete introduction to recent advances in preprocessing analysis, or kernelization, with extensive examples using a single data set.
Idioma: Inglés
Publicado por Cambridge University Press, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 178,09
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Añadir al carritoCondición: New. 2019. Hardcover. . . . . .
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 86,56
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Añadir al carritoHardcover. Condición: new. Hardcover. 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. This self-contained introduction to kernelization, a rapidly developing area of preprocessing analysis, is for researchers, professionals, and graduate students in computer science and optimization. It includes recent advances in upper and lower bounds and meta-theorems, and demonstrates methods through extensive examples using a single data set. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 79,59
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Añadir al carritoHardcover. Condición: Brand New. 515 pages. 9.50x6.25x1.25 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
Librería: CitiRetail, Stevenage, Reino Unido
EUR 90,34
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: new. Hardcover. 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. This self-contained introduction to kernelization, a rapidly developing area of preprocessing analysis, is for researchers, professionals, and graduate students in computer science and optimization. It includes recent advances in upper and lower bounds and meta-theorems, and demonstrates methods through extensive examples using a single data set. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Librería: moluna, Greven, Alemania
EUR 87,58
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This self-contained introduction to kernelization, a rapidly developing area of preprocessing analysis, is for researchers, professionals, and graduate students in computer science and optimization. It includes recent advances in upper and lower bounds and .
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2019
ISBN 10: 1107057760 ISBN 13: 9781107057760
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 128,04
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: new. Hardcover. 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. This self-contained introduction to kernelization, a rapidly developing area of preprocessing analysis, is for researchers, professionals, and graduate students in computer science and optimization. It includes recent advances in upper and lower bounds and meta-theorems, and demonstrates methods through extensive examples using a single data set. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.