9781107057760 - kernelization: theory of parameterized preprocessing de fomin, fedor v.; lokshtanov, daniel; saurabh, saket; zehavi, meirav (15 resultados)
Kernelization: Theory of Parameterized Preprocessing
Fomin, Fedor V.; Lokshtanov, Daniel; Saurabh, Saket; Zehavi, Meirav
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Librería: California Books, Miami, Estados Unidos de AmericaCalifornia Books
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EUR 86,57
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Librería: Books Puddle, New York, Estados Unidos de AmericaBooks Puddle
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EUR 85,10
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Librería: Biblios, frankfurt am main, AlemaniaBiblios
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EUR 83,37
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Kernelization: Theory of Parameterized Preprocessing
Fomin, Fedor V.; Lokshtanov, Daniel; Saurabh, Saket; Zehavi, Meirav
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 83,54
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Kernelization: Theory of Parameterized Preprocessing
Fomin, Fedor V./ Lokshtanov, Daniel/ Saurabh, Saket/ Zehavi, Meirav
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 118,61
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Hardcover. Condición: Brand New. 515 pages. 9.50x6.25x1.25 inches. In Stock.
Kernelization: Theory of Parameterized Preprocessing
Fomin, Fedor V., Lokshtanov, Daniel, Saurabh, Saket, Zehavi,
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Librería: Mispah books, Redhill, Reino UnidoMispah books
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EUR 104,61
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Hardcover. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Librería: Kennys Bookstore, Olney, Estados Unidos de AmericaKennys Bookstore
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EUR 153,20
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Condición: New. 2019. Hardcover. . . . . . Books ship from the US and Ireland.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 98,66
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Buch. 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.
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 178,09
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Condición: New. 2019. Hardcover. . . . . .
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Librería: Grand Eagle Retail, Bensenville, Estados Unidos de AmericaGrand Eagle Retail
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EUR 86,56
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Hardcover. 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.
Kernelization: Theory of Parameterized Preprocessing
Fomin, Fedor V./ Lokshtanov, Daniel/ Saurabh, Saket/ Zehavi, Meirav
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- Impresión bajo demanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 79,59
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Hardcover. Condición: Brand New. 515 pages. 9.50x6.25x1.25 inches. In Stock. This item is printed on demand.
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 90,34
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Hardcover. 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.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 87,58
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Condición: 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 .
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Librería: AussieBookSeller, Truganina, AustraliaAussieBookSeller
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EUR 128,04
Envío por EUR 31,73Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. 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.
