Data structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines.
A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert.
With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.
Table of Contents:
Preface xi
Acknowledgments xvii
Chapter 13 Hash Tables 1
13.1 Introduction 1
13.1.1 Dictionaries 1
13.2 Hash table structure 2
13.3 Hash functions 4
13.3.1 Building hash functions 4
13.4 Linear open addressing 5
13.4.1 Operations on linear open addressed hash tables 8
13.4.2 Performance analysis 10
13.4.3 Other collision resolution techniques with open addressing 11
13.5 Chaining 13
13.5.1 Operations on chained hash tables 15
13.5.2 Performance analysis 17
13.6 Applications 18
13.6.1 Representation of a keyword table in a compiler 18
13.6.2 Hash tables in the evaluation of a join operation on relational databases 19
13.6.3 Hash tables in a direct file organization 22
13.7 Illustrative problems 23
Chapter 14 File Organizations 33
14.1 Introduction 33
14.2 Files 34
14.3 Keys 36
14.4 Basic file operations 38
14.5 Heap or pile organization 38
14.5.1 Insert, delete and update operations 39
14.6 Sequential file organization 39
14.6.1 Insert, delete and update operations 39
14.6.2 Making use of overflow blocks 40
14.7 Indexed sequential file organization 41
14.7.1 Structure of the ISAM files 41
14.7.2 Insert, delete and update operations for a naïve ISAM file 42
14.7.3 Types of indexing 43
14.8 Direct file organization 48
14.9 Illustrative problems 50
Chapter 15 k-d Trees and Treaps 61
15.1 Introduction 61
15.2 k-d trees: structure and operations 61
15.2.1 Construction of a k-d tree 65
15.2.2 Insert operation on k-d trees 69
15.2.3 Find minimum operation on k-d trees 70
15.2.4 Delete operation on k-d trees 72
15.2.5 Complexity analysis and applications of k-d trees 74
15.3 Treaps: structure and operations 76
15.3.1 Treap structure 76
15.3.2 Operations on treaps 77
15.3.3 Complexity analysis and applications of treaps 82
15.4 Illustrative problems 83
Chapter 16 Searching 93
16.1 Introduction 93
16.2 Linear search 94
16.2.1 Ordered linear search 94
16.2.2 Unordered linear search 94
16.3 Transpose sequential search 96
16.4 Interpolation search 98
16.5 Binary search 100
16.5.1 Decision tree for binary search 101
16.6 Fibonacci search 104
16.6.1 Decision tree for Fibonacci search 105
16.7 Skip list search 108
16.7.1 Implementing skip lists 112
16.7.2 Insert operation in a skip list 113
16.7.3 Delete operation in a skip list 114
16.8 Other search techniques 116
16.8.1 Tree search 116
16.8.2 Graph search 116
16.8.3 Indexed sequential search 116
16.9 Illustrative problems 118
Chapter 17 Internal Sorting 131
17.1 Introduction 131
17.2 Bubble sort 132
17.2.1 Stability and performance analysis 134
17.3 Insertion sort 135
17.3.1 Stability and performance analysis 136
17.4 Selection sort 138
17.4.1 Stability and performance analysis 140
17.5 Merge sort 140
17.5.1 Two-way merging 141
17.5.2 k-way merging 143
17.5.3 Non-recursive merge sort procedure 144
17.5.4 Recursive merge sort procedure 145
17.6 Shell sort 147
17.6.1 Analysis of shell sort 153
17.7 Quick sort 153
17.7.1 Partitioning 153
17.7.2 Quick sort procedure 156
17.7.3 Stability and performance analysis 158
17.8 Heap sort 159
17.8.1 Heap 159
17.8.2 Construction of heap 160
17.8.3 Heap sort procedure 163
17.8.4 Stability and performance analysis 167
17.9 Radix sort 167
17.9.1 Radix sort method 167
17.9.2 Most significant digit first sort 171
17.9.3 Performance analysis 171
17.10 Counting sort 171
17.10.1 Performance analysis 175
17.11 Bucket sort 175
17.11.1 Performance analysis 178
17.12 Illustrative problems 179
Chapter 18 External Sorting 197
18.1 Introduction 197
18.1.1 The principle behind external sorting 197
18.2 External storage devices 198
18.2.1 Magnetic tapes 199
18.2.2 Magnetic disks 200
18.3 Sorting with tapes: balanced merge 202
18.3.1 Buffer handling 204
18.3.2 Balanced P-way merging on tapes 205
18.4 Sorting with disks: balanced merge 206
18.4.1 Balanced k-way merging on disks 207
18.4.2 Selection tree 208
18.5 Polyphase merge sort 212
18.6 Cascade merge sort 214
18.7 Illustrative problems 216
Chapter 19 Divide and Conquer 229
19.1 Introduction 229
19.2 Principle and abstraction 229
19.3 Finding maximum and minimum 231
19.3.1 Time complexity analysis 232
19.4 Merge sort 233
19.4.1 Time complexity analysis 233
19.5 Matrix multiplication 234
19.5.1 Divide and Conquer-based approach to “high school” method of matrix multiplication 234
19.5.2 Strassen’s matrix multiplication algorithm 236
19.6 Illustrative problems 239
Chapter 20 Greedy Method 245
20.1 Introduction 245
20.2 Abstraction 245
20.3 Knapsack problem 246
20.3.1 Greedy solution to the knapsack problem 247
20.4 Minimum cost spanning tree algorithms 249
20.4.1 Prim’s algorithm as a greedy method 250
20.4.2 Kruskal’s algorithm as a greedy method 250
20.5 Dijkstra’s algorithm 251
20.6 Illustrative problems 251
Chapter 21 Dynamic Programming 261
21.1 Introduction 261
21.2 0/1 knapsack problem 263
21.2.1 Dynamic programming-based solution 264
21.3 Traveling salesperson problem 266
21.3.1 Dynamic programming-based solution 267
21.3.2 Time complexity analysis and applications of traveling salesperson problem 269
21.4 All-pairs shortest path problem 269
21.4.1 Dynamic programming-based solution 270
21.4.2 Time complexity analysis 272
21.5 Optimal binary search trees 272
21.5.1 Dynamic programming-based solution 274
21.5.2 Construction of the optimal binary search tree 276
21.5.3 Time complexity analysis 279
21.6 Illustrative problems 280
Chapter 22 P and NP Class of Problems 287
22.1 Introduction 287
22.2 Deterministic and nondeterministic algorithms 289
22.3 Satisfiability problem 292
22.3.1 Conjunctive normal form and Disjunctive normal form 294
22.3.2 Definition of the satisfiability problem 294
22.3.3 Construction of CNF and DNF from a logical formula 295
22.3.4 Transformation of a CNF into a 3-CNF 296
22.3.5 Deterministic algorithm for the satisfiability problem 297
22.3.6 Nondeterministic algorithm for the satisfiability problem 297
22.4 NP-complete and NP-hard problems 297
22.4.1 Definitions 298
22.5 Examples of NP-hard and NP-complete problems 300
22.6 Cook’s theorem 302
22.7 The unsolved problem P = NP 303
22.8 Illustrative problems 304
References 311
Index 313
Summaries of other volumes 317
