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Algorithm Engineering de Michael T. Goodrich

Algorithm Engineering

Autor Michael T. Goodrich, Roberto Tamassia
en Limba Engleză Paperback – oct 2001
Alles ber die Implementation von Datenstrukturen und Algorithmen: Die Autoren zeigen Ihnen, dass Sie mehr bentigen als nur theoretisch-algorithmische Kenntnisse, nmlich auch ingenieurtechnische Entwurfsprinzipien: abstrakte Datentypen, objektorientierte Entwurfsmuster und Strategien, die Robustheit und Benutzerfreundlichkeit sichern. Behandelt werden u.a. die schnelle Fourier-Transformation (FFT), Kryptologie, Parallelitt und NP-Vollstndigkeit.
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Specificații

ISBN-13: 9780471383659
ISBN-10: 0471383651
Pagini: 722
Ilustrații: illustrations
Dimensiuni: 191 x 235 x 38 mm
Greutate: 1.32 kg
Ediția:00003
Editura: Wiley
Locul publicării:Hoboken, United States

Public țintă

Computer Programmers, Software Engineers and Scientists.

Notă biografică

Michael T. Goodrich is a mathematician and computer scientist. He is a Chancellor's Professor and the chair of Department of Computer Science, of Donald Bren School of Information and Computer Sciences, a school of University of California, Irvine. Roberto Tamassia is the author of Algorithm Design: Foundations, Analysis, and Internet Examples, published by Wiley.

Cuprins

I Fundamental Tools 1
1 Algorithm Analysis 3
1.1 Methodologies for Analyzing Algorithms  5
1.2 Asymptotic Notation 13
1.3 A Quick Mathematical Review  21
1.4 Case Studies in Algorithm Analysis  31
1.5 Amortization  34
1.6 Experimentation  42
1.7 Exercises  47
2 Basic Data Structures 55
2.1 Stack sand Queues  57
2.2 Vectors, Lists, and Sequences  65
2.3 Trees  75
2.4 Priority Queues and Heaps  94
2.5 Dictionaries and Hash Tables  114
2.6 Java Example: Heap  128
2.7 Exercises  131
3 Search Trees and Skip Lists 139
3.1 Ordered Dictionaries and Binary Search Trees  141
3.2 AVL Trees  152
3.3 Bounded-Depth Search Trees  159
3.4 Splay Trees  185
3.5 Sk i p Lists  195
3.6 Java Example: AVL and Red-Black Trees  202
3.7 Exercises  212
4 Sorting, Sets, and Selection 217
4.1 Merge-Sort  219
4.2 The Set Abstract Data Type  225
4.3 Quick -Sort  235
4.4 A Lower Bound on Comparison-Based Sorting  239
4.5 Buck et-Sort and Radix-Sort  241
4.6 Comparison of Sorting Algorithms  244
4.7 Selection  245
4.8 Java Example: In-Place Quick -Sort  248
4.9 Exercises  251
5 Fundamental Techniques 257
5.1 The GreedyMethod  259
5.2 Divide-and-Conquer  263
5.3 Dynamic Programming  274
5.4 Exercises  282
II Graph Algorithms 285
6 Graphs 287
6.1 The Graph Abstract Data Type  289
6.2 Data Structures for Graphs  296
6.3 Graph Traversal  303
6.4 Directed Graphs  316
6.5 Java Example: Depth-First Search  329
6.6 Exercises  335
7 Weighted Graphs 339
7.1 Single-Source Shortest Paths  341
7.2 All-Pairs Shortest Paths  354
7.3 Minimum Spanning Trees  360
7.4 Java Example: Dijk stra's Algorithm  373
7.5 Exercises  376
8 Network Flow and Matching 381
8.1 Flows and Cuts  383
8.2 Maximum Flow  387
8.3 Maximum BipartiteMatching  396
8.4 Minimum-Cost Flow  398
8.5 Java Example: Minimum-Cost Flow  405
8.6 Exercises  412
III Internet Algorithmics 415
9 Text Processing 417
9.1 Strings and PatternMatching Algorithms  419
9.2 Tries  429
9.3 Text Compression  440
9.4 Text Similarity Testing  443
9.5 Exercises  447
10 Number Theory and Cryptography 451
10.1 Fundamental Algorithms Involving Numbers  453
10.2 Cryptographic Computations  471
10.3 Information Security Algorithms and Protocols  481
10.4 The Fast Fourier Transform  488
10.5 Java Example: FFT  500
10.6 Exercises  508
11 Network Algorithms 511
11.1 ComplexityMeasures and Models  513
11.2 Fundamental Distributed Algorithms  517
11.3 Broadcast and Unicast Routing  530
11.4 Multicast Routing  535
11.5 Exercises  541
IV Additional Topics 545
12 Computational Geometry 547
12.1 Range Trees  549
12.2 Priority Search Trees  556
12.3 Quadtrees and k-D Trees  561
12.4 The Plane Sweep Technique  565
12.5 Convex Hulls  572
12.6 Java Example: Convex Hull  583
12.7 Exercises  587
13 NP-Completeness 591
13.1 P and NP  593
13.2 NP-Completeness  599
13.3 Important NP-Complete Problems  603
13.4 Approximation Algorithms  618
13.5 Back track i ng and Branch-and-Bound  627
13.6 Exercises  638
14 Algorithmic Frameworks 643
14.1 External-Memory Algorithms  645
14.2 Parallel Algorithms  657
14.3 Online Algorithms  667
14.4 Exercises  680
A Useful Mathematical Facts 685
Bibliography 689
Index 698