A Practical Introduction to Data Structures and Algorithm Analysis

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Bibliography[AG06] Ken Arnold and James Gosling. The Java Programming Language.Addison-Wesley, Reading, MA, USA, fourth edition, 2006.[Aha00] Dan Aharoni. Cogito, ergo sum! cognitive processes of students dealingwith data structures. In Proceedings of SIGCSE’00, pages 26–30,ACM Press, March 2000.[AHU74] Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullman. The Designand Analysis of Computer Algorithms. Addison-Wesley, Reading, MA,1974.[AHU83] Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullman. Data Structuresand Algorithms. Addison-Wesley, Reading, MA, 1983.[BB96] G. Brassard and P. Bratley. Fundamentals of Algorithmics. PrenticeHall, Upper Saddle River, NJ, 1996.[Ben75] John Louis Bentley. Multidimensional binary search trees used forassociative searching. Communications of the ACM, 18(9):509–517,September 1975. ISSN: 0001-0782.[Ben82] John Louis Bentley. Writing Efficient Programs. Prentice Hall, UpperSaddle River, NJ, 1982.[Ben84] John Louis Bentley. Programming pearls: The back of the envelope.Communications of the ACM, 27(3):180–184, March 1984.[Ben85] John Louis Bentley. Programming pearls: Thanks, heaps. Communicationsof the ACM, 28(3):245–250, March 1985.[Ben86] John Louis Bentley. Programming pearls: The envelope is back. Communicationsof the ACM, 29(3):176–182, March 1986.[Ben88] John Bentley. More Programming Pearls: Confessions of a Coder.Addison-Wesley, Reading, MA, 1988.[Ben00] John Bentley. Programming Pearls. Addison-Wesley, Reading, MA,second edition, 2000.593
594 BIBLIOGRAPHY[BG00] Sara Baase and Allen Van Gelder. Computer Algorithms: Introductionto Design & Analysis. Addison-Wesley, Reading, MA, USA, thirdedition, 2000.[BM85] John Louis Bentley and Catherine C. McGeoch. Amortized analysisof self-organizing sequential search heuristics. Communications of theACM, 28(4):404–411, April 1985.[Bro95] Frederick P. Brooks. The Mythical Man-Month: Essays on SoftwareEngineering, 25th Anniversary Edition. Addison-Wesley, Reading,MA, 1995.[BSTW86] John Louis Bentley, Daniel D. Sleator, Robert E. Tarjan, and Victor K.Wei. A locally adaptive data compression scheme. Communicationsof the ACM, 29(4):320–330, April 1986.[CLRS01] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and CliffordStein. Introduction to Algorithms. The MIT Press, Cambridge,MA, second edition, 2001.[Com79] Douglas Comer. The ubiquitous B-tree. Computing Surveys,11(2):121–137, June 1979.[ECW92] Vladimir Estivill-Castro and Derick Wood. A survey of adaptive sortingalgorithms. Computing Surveys, 24(4):441–476, December 1992.[ED88] R.J. Enbody and H.C. Du. Dynamic hashing schemes. ComputingSurveys, 20(2):85–113, June 1988.[Epp04] Susanna S. Epp. Discrete Mathematics with Applications. Brooks/ColePublishing Company, Pacific Grove, CA, third edition, 2004.[FBY92] W.B. Frakes and R. Baeza-Yates, editors. Information Retrieval: DataStructures & Algorithms. Prentice Hall, Upper Saddle River, NJ, 1992.[FF89] Daniel P. Friedman and Matthias Felleisen. The Little LISPer. MacmillanPublishing Company, New York, 1989.[FHCD92] Edward A. Fox, Lenwood S. Heath, Q. F. Chen, and Amjad M. Daoud.Practical minimal perfect hash functions for large databases. Communicationsof the ACM, 35(1):105–121, January 1992.[FL95] H. Scott Folger and Steven E. LeBlanc. Strategies for Creative ProblemSolving. Prentice Hall, Upper Saddle River, NJ, 1995.[Fla05] David Flanagan. Java in a Nutshell. O’Reilly & Associates, Inc.,Sebatopol, CA, 5th edition, 2005.[FZ98] M.J. Folk and B. Zoellick. File Structures: An Object-Oriented Approachwith C++. Addison-Wesley, Reading, MA, third edition, 1998.[GHJV95] Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides.Design Patterns: Elements of Reusable Object-Oriented Software.Addison-Wesley, Reading, MA, 1995.
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xviPrefacemake the examples as clea
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xviiiPrefaceOne of the most importa
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xxPrefaceOthers at Prentice Hall wh
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1Data Structures and AlgorithmsHow
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Sec. 1.1 A Philosophy of Data Struc
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Sec. 1.1 A Philosophy of Data Struc
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Sec. 1.2 Abstract Data Types and Da
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Sec. 1.2 Abstract Data Types and Da
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Sec. 1.3 Design Patterns 13Data Typ
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Sec. 1.3 Design Patterns 151.3.3 Co
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Sec. 1.4 Problems, Algorithms, and
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Sec. 1.5 Further Reading 194. It mu
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Sec. 1.6 Exercises 21If you want to
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Sec. 1.6 Exercises 231.12 Imagine t
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2Mathematical PreliminariesThis cha
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Sec. 2.1 Sets and Relations 27examp
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Sec. 2.2 Miscellaneous Notation 29x
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Sec. 2.3 Logarithms 31positive inte
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Sec. 2.4 Summations and Recurrences
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Sec. 2.4 Summations and Recurrences
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Sec. 2.5 Recursion 37factorial of n
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Sec. 2.6 Mathematical Proof Techniq
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Sec. 2.7 Estimating 47Example 2.17
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Sec. 2.8 Further Reading 49typical
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Sec. 2.9 Exercises 51(f) {〈2, 1
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Sec. 2.9 Exercises 532.17 Prove by
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Sec. 2.9 Exercises 552.38 When buyi
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3Algorithm AnalysisHow long will it
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Sec. 3.1 Introduction 59compiled wi
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Sec. 3.1 Introduction 61Example 3.3
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Sec. 3.2 Best, Worst, and Average C
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Sec. 3.3 A Faster Computer, or a Fa
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Sec. 3.4 Asymptotic Analysis 67comp
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Sec. 3.4 Asymptotic Analysis 69fast
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Sec. 3.4 Asymptotic Analysis 71Exam
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Sec. 3.4 Asymptotic Analysis 73Rule
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Sec. 3.5 Calculating the Running Ti
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Sec. 3.5 Calculating the Running Ti
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Sec. 3.6 Analyzing Problems 79binar
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Sec. 3.7 Common Misunderstandings 8
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Sec. 3.9 Space Bounds 83pixel. Assu
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Sec. 3.9 Space Bounds 85A classic e
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Sec. 3.10 Speeding Up Your Programs
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Sec. 3.11 Empirical Analysis 893.11
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Sec. 3.13 Exercises 913.3 Arrange t
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Sec. 3.13 Exercises 93(f) sum = 0;f
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Sec. 3.14 Projects 95a summation fo
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4Lists, Stacks, and QueuesIf your p
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Sec. 4.1 Lists 101The next step is
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Sec. 4.1 Lists 103mentations, asser
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Sec. 4.1 Lists 105/** Array-based l
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Sec. 4.1 Lists 107Insert 23:1312 20
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Sec. 4.1 Lists 109currtailheadFigur
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Sec. 4.1 Lists 111// Linked list im
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Sec. 4.1 Lists 113curr... 23 12 ...
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Sec. 4.1 Lists 115// Singly linked
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Sec. 4.1 Lists 117determined size.
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Sec. 4.1 Lists 119pointer to each e
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Sec. 4.1 Lists 121// Insert "it" at
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Sec. 4.1 Lists 123curr... 20curr23
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Sec. 4.2 Stacks 125/** Stack ADT */
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Sec. 4.2 Stacks 127// Linked stack
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Sec. 4.2 Stacks 129Currptr β 1Curr
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Sec. 4.2 Stacks 131Example 4.3 The
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Sec. 4.3 Queues 133/** Queue ADT */
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Sec. 4.3 Queues 135frontfront20 512
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Sec. 4.3 Queues 137// Array-based q
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Sec. 4.4 Dictionaries 139enqueue pl
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Sec. 4.4 Dictionaries 141Method cle
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Sec. 4.4 Dictionaries 143// Contain
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Sec. 4.4 Dictionaries 145/** Dictio
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Sec. 4.5 Further Reading 1474.5 Fur
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Sec. 4.6 Exercises 149(a) The data
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Sec. 4.7 Projects 1514.3 Use singly
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5Binary TreesThe list representatio
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Sec. 5.1 Definitions and Properties
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Sec. 5.1 Definitions and Properties
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Sec. 5.2 Binary Tree Traversals 159
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Sec. 5.2 Binary Tree Traversals 161
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Sec. 5.3 Binary Tree Node Implement
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Sec. 5.4 Binary Search Trees 171Thi
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Sec. 5.4 Binary Search Trees 173120
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Sec. 5.4 Binary Search Trees 175/**
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Sec. 5.4 Binary Search Trees 177min
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Sec. 5.4 Binary Search Trees 17937
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Sec. 5.5 Heaps and Priority Queues
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Sec. 5.6 Huffman Coding Trees 189Le
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Sec. 5.6 Huffman Coding Trees 191St
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Sec. 5.6 Huffman Coding Trees 193/*
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Sec. 5.6 Huffman Coding Trees 195//
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Sec. 5.6 Huffman Coding Trees 197A
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Sec. 5.8 Exercises 199Many techniqu
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Sec. 5.8 Exercises 2015.19 Write a
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Sec. 5.9 Projects 203(d) The record
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6Non-Binary TreesMany organizations
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Sec. 6.1 General Tree Definitions a
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Sec. 6.2 The Parent Pointer Impleme
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Sec. 6.3 General Tree Implementatio
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Sec. 6.3 General Tree Implementatio
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Sec. 6.4 K-ary Trees 221RRABABCDEFC
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Sec. 6.5 Sequential Tree Implementa
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Sec. 6.5 Sequential Tree Implementa
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Sec. 6.7 Exercises 2276.2 Write an
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Sec. 6.7 Exercises 229CAFBEHGDIFigu
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Sec. 6.8 Projects 231proved perform
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7Internal SortingWe sort many thing
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Sec. 7.2 Three Θ(n 2 ) Sorting Alg
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Sec. 7.3 Shellsort 24559 20 17 13 2
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Sec. 7.4 Mergesort 24736 20 17 13 2
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Sec. 7.5 Quicksort 249static
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Sec. 7.5 Quicksort 251static
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Sec. 7.5 Quicksort 253InitialPass 1
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Sec. 7.5 Quicksort 255The initial c
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Sec. 7.6 Heapsort 257and fast. The
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Sec. 7.7 Binsort and Radix Sort 259
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Sec. 7.8 An Empirical Comparison of
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Sec. 7.9 Lower Bounds for Sorting 2
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Sec. 7.9 Lower Bounds for Sorting 2
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Sec. 7.10 Further Reading 271• A
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Sec. 7.11 Exercises 2737.7 Recall t
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Sec. 7.12 Projects 2757.16 (a) Devi
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Sec. 7.12 Projects 277classmates? W
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280 Chap. 8 File Processing and Ext
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318 Chap. 9 SearchingThis and the f
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320 Chap. 9 Searchingelement in L,
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322 Chap. 9 SearchingThis is equal
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324 Chap. 9 Searching9.2 Self-Organ
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326 Chap. 9 SearchingWhen a frequen
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328 Chap. 9 Searchingand the total
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330 Chap. 9 SearchingThe set differ
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332 Chap. 9 Searchingprobability th
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334 Chap. 9 SearchingExample 9.6 A
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336 Chap. 9 Searchingand the next f
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338 Chap. 9 Searching01HashTable100
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340 Chap. 9 Searching/** Insert rec
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342 Chap. 9 Searching09050090501100
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344 Chap. 9 SearchingExample 9.9 Co
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346 Chap. 9 Searchingproject at the
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348 Chap. 9 SearchingIf N and M are
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350 Chap. 9 SearchingDepending on t
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352 Chap. 9 SearchingBentley et al.
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354 Chap. 9 Searching(c) How many s
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356 Chap. 9 Searching9.5 Implement
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358 Chap. 10 Indexingfile is create
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360 Chap. 10 Indexing1 2003 5894 10
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362 Chap. 10 IndexingSecondaryKeyJo
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364 Chap. 10 Indexingkey value for
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366 Chap. 10 IndexingFigure 10.7 Br
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368 Chap. 10 Indexing/** 2-3 tree n
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370 Chap. 10 Indexing18331223 30 48
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372 Chap. 10 Indexingprivate TTNode
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374 Chap. 10 Indexing24152033 45 48
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376 Chap. 10 Indexingvalues, but th
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378 Chap. 10 Indexing331012 233348
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380 Chap. 10 Indexing4845 4748 50 5
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382 Chap. 10 IndexingAs mentioned e
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384 Chap. 10 Indexing(b) Show the i
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PART IVAdvanced Data Structures387
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390 Chap. 11 Graphs04123147123(a)(b
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392 Chap. 11 Graphs0 1 2 3 40201 11
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394 Chap. 11 GraphsThe adjacency ma
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396 Chap. 11 Graphsedge list. Funct
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398 Chap. 11 Graphspublic int weigh
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400 Chap. 11 Graphs}// Store edge w
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402 Chap. 11 GraphsABABCCDDFFEE(a)(
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404 Chap. 11 Graphs// Breadth first
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406 Chap. 11 GraphsAInitial call to
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408 Chap. 11 Graphsstatic void tops
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410 Chap. 11 Graphs// Compute short
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412 Chap. 11 Graphs// Dijkstra’s
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414 Chap. 11 Graphs// Compute a min
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416 Chap. 11 GraphsMarkedVertices v
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418 Chap. 11 GraphsInitialA B C D E
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420 Chap. 11 Graphs2 511032041032 6
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422 Chap. 11 Graphstriangular matri
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424 Chap. 12 Lists and Arrays Revis
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448 Chap. 13 Advanced Tree Structur
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PART VTheory of Algorithms479
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482 Chap. 14 Analysis Techniquesthi
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484 Chap. 14 Analysis TechniquesExa
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486 Chap. 14 Analysis Techniquesthe
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488 Chap. 14 Analysis Techniquesof
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490 Chap. 14 Analysis TechniquesHow
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492 Chap. 14 Analysis Techniques= 2
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494 Chap. 14 Analysis TechniquesNot
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498 Chap. 14 Analysis Techniquesbit
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500 Chap. 14 Analysis Techniquesfro
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502 Chap. 14 Analysis Techniques14.
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504 Chap. 14 Analysis Techniques}G.
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15Lower BoundsHow do I know if I ha
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Sec. 15.1 Introduction to Lower Bou
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Sec. 15.2 Lower Bounds on Searching
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Sec. 15.3 Finding the Maximum Value
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Sec. 15.4 Adversarial Lower Bounds
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Sec. 15.4 Adversarial Lower Bounds
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Sec. 15.5 State Space Lower Bounds
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Sec. 15.5 State Space Lower Bounds
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Sec. 15.6 Finding the ith Best Elem
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Sec. 15.7 Optimal Sorting 525search
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Sec. 15.8 Further Reading 527Merge
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Sec. 15.9 Exercises 52915.10 Show t
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16Patterns of AlgorithmsThis chapte
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Sec. 16.1 Dynamic Programming 533dy
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Sec. 16.1 Dynamic Programming 535ar
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Sec. 16.2 Randomized Algorithms 537
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Page 627 and 628: 608 INDEXterminology, 236-237spatia
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A Practical Introduction to Data Structures and Algorithm Analysis

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