9780817633288 - Lecture Notes on Bucket Algorithms: 6 di Devroye, Luc (17 risultati)

Softcover. Condizione: Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 1985. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Edition 1985. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Condizione: Near Fine. hardback book in near fine conition.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New. In.

Condizione: New. pp. 160.

PF. Condizione: New.

Condizione: New. pp. 160 23:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on White w/Gloss Lam.

Condizione: New. Series: Progress in Computer Science and Applied Logic. Num Pages: 155 pages, biography. BIC Classification: YQS. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 9. Weight in Grams: 380. . 1985. Softcover reprint of the original 1st ed. 1986. Paperback. . . . .

Condizione: New. pp. 160.

Hardcover. Condizione: Brand New. 155 pages. 8.90x5.90x0.50 inches. In Stock.

Condizione: New. Series: Progress in Computer Science and Applied Logic. Num Pages: 155 pages, biography. BIC Classification: YQS. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 9. Weight in Grams: 380. . 1985. Softcover reprint of the original 1st ed. 1986. Paperback. . . . . Books ship from the US and Ireland.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Hashing algorithms scramble data and create pseudo-uniform data distribu tions. Bucket algorithms operate on raw untransformed data which are parti tioned Into groups according to membership In equl-slzed d-dlmenslonal hyperrec tangles, called cells or buckets. The bucket data structure Is rather sensitive to the distribution of the data. In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the dis tribution of the data. The results are Illustrated on standard searching, sorting and selection problems, as well as on a variety of problems In computational geometry and operations research. The notes grew partially from a graduate course on probability theory In computer science. I wish to thank Elizabeth Van Gulick for her help with the manuscript, and David Avis, Hanna AYukawa, Vasek Chvatal, Beatrice Devroye, Hossam EI Glndy, Duncan McCallum, Magda McCallum, Godfrled Toussaint and Sue Whltesldes'for making the School of Computer Science at McGill University such an enjoyable place. The work was supported by NSERC Grant A3456 and by FCAC Grant EQ-1679. INTRODUCTION 1 INTRODUCTION It Is not a secret that methods based upon the truncation of data have good expected time performance. For example, for nice distributions of the data, searching Is often better done via a hashing data structure Instead of via a search tree. The speed one observes In practice Is due to the fact that the truncation operation Is a constant time operation.

Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Hashing algorithms scramble data and create pseudo-uniform data distribu­ tions. Bucket algorithms operate on raw untransformed data which are parti­ tioned Into groups according to membership In equl-slzed d-dlmenslonal hyperrec­ tangles, called cells or buckets. The bucket data structure Is rather sensitive to the distribution of the data. In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the dis­ tribution of the data. The results are Illustrated on standard searching, sorting and selection problems, as well as on a variety of problems In computational geometry and operations research. The notes grew partially from a graduate course on probability theory In computer science. I wish to thank Elizabeth Van Gulick for her help with the manuscript, and David Avis, Hanna AYukawa, Vasek Chvatal, Beatrice Devroye, Hossam EI Glndy, Duncan McCallum, Magda McCallum, Godfrled Toussaint and Sue Whltesldes"for making the School of Computer Science at McGill University such an enjoyable place. The work was supported by NSERC Grant A3456 and by FCAC Grant EQ-1679. INTRODUCTION 1 INTRODUCTION It Is not a secret that methods based upon the truncation of data have good expected time performance. For example, for nice distributions of the data, searching Is often better done via a hashing data structure Instead of via a search tree. The speed one observes In practice Is due to the fact that the truncation operation Is a constant time operation.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Hashing algorithms scramble data and create pseudo-uniform data distribu tions. Bucket algorithms operate on raw untransformed data which are parti tioned Into groups according to membership In equl-slzed d-dlmenslonal hyperrec tangles, called cells or buck.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Hashing algorithms scramble data and create pseudo-uniform data distribu tions. Bucket algorithms operate on raw untransformed data which are parti tioned Into groups according to membership In equl-slzed d-dlmenslonal hyperrec tangles, called cells or buckets. The bucket data structure Is rather sensitive to the distribution of the data. In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the dis tribution of the data. The results are Illustrated on standard searching, sorting and selection problems, as well as on a variety of problems In computational geometry and operations research. The notes grew partially from a graduate course on probability theory In computer science. I wish to thank Elizabeth Van Gulick for her help with the manuscript, and David Avis, Hanna AYukawa, Vasek Chvatal, Beatrice Devroye, Hossam EI Glndy, Duncan McCallum, Magda McCallum, Godfrled Toussaint and Sue Whltesldes'for making the School of Computer Science at McGill University such an enjoyable place. The work was supported by NSERC Grant A3456 and by FCAC Grant EQ-1679. INTRODUCTION 1 INTRODUCTION It Is not a secret that methods based upon the truncation of data have good expected time performance. For example, for nice distributions of the data, searching Is often better done via a hashing data structure Instead of via a search tree. The speed one observes In practice Is due to the fact that the truncation operation Is a constant time operation. 160 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Hashing algorithms scramble data and create pseudo-uniform data distribu tions. Bucket algorithms operate on raw untransformed data which are parti tioned Into groups according to membership In equl-slzed d-dlmenslonal hyperrec tangles, called cells or buckets. The bucket data structure Is rather sensitive to the distribution of the data. In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the dis tribution of the data. The results are Illustrated on standard searching, sorting and selection problems, as well as on a variety of problems In computational geometry and operations research. The notes grew partially from a graduate course on probability theory In computer science. I wish to thank Elizabeth Van Gulick for her help with the manuscript, and David Avis, Hanna AYukawa, Vasek Chvatal, Beatrice Devroye, Hossam EI Glndy, Duncan McCallum, Magda McCallum, Godfrled Toussaint and Sue Whltesldes'for making the School of Computer Science at McGill University such an enjoyable place. The work was supported by NSERC Grant A3456 and by FCAC Grant EQ-1679. INTRODUCTION 1 INTRODUCTION It Is not a secret that methods based upon the truncation of data have good expected time performance. For example, for nice distributions of the data, searching Is often better done via a hashing data structure Instead of via a search tree. The speed one observes In practice Is due to the fact that the truncation operation Is a constant time operation.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 160 pp. Englisch.