Data Structures and Algorithms Made Easy By Narasimha Karumanchi Pdf

Data Structures and Algorithms Made Easy By Narasimha Karumanchi Pdf

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Data Structures and Algorithms Made Easy By Narasimha Karumanchi Pdf

Acknowledgements

Mother and Father, it’s insolvable to thank you adequately for everything you have done, from loving me unconditionally to raising me in a stable ménage, where your patient sweats and traditional values tutored your children to celebrate and embrace life.

 I couldn’t have asked for better parents or part- models. You showed me that anything is possible with faith, hard work and determination. This book would not have been possible without the help of numerous people.

 I would like to express my gratefulness to all of the people who handed support, talked effects over, read, wrote, offered commentary, allowed me to quote their reflections and supported in the editing, proofreading and design. In particular, I would like to thank the following individualities

Table of Contents

  1. Introduction 1.1 Variables 1.2 Data Types 1.3 Data Structures 1.4 Abstract Data Types (ADTs) 1.5 What is an Algorithm? 1.6 Why the Analysis of Algorithms? 1.7 Goal of the Analysis of Algorithms 1.8 What is Running Time Analysis? 1.9 How to Compare Algorithms 1.10 What is Rate of Growth? 1.11 Commonly Used Rates of Growth 1.12 Types of Analysis 1.13 Asymptotic Notation 1.14 Big-O Notation [Upper Bounding Function] 1.15 Omega-Q Notation [Lower Bounding Function] 1.16 Theta-Θ Notation [Order Function] 1.17 Important Notes 1.18 Why is it called Asymptotic Analysis? 1.19 Guidelines for Asymptotic Analysis 1.20 Simplifying properties of asymptotic notations 1.21 Commonly used Logarithms and Summations 1.22 Master Theorem for Divide and Conquer Recurrences 1.23 Divide and Conquer Master Theorem: Problems & Solutions 1.24 Master Theorem for Subtract and Conquer Recurrences 1.25 Variant of Subtraction and Conquer Master Theorem 1.26 Method of Guessing and Confirming 1.27 Amortized Analysis 1.28 Algorithms Analysis: Problems & Solutions 2. Recursion and Backtracking 2.1 Introduction 2.2 What is Recursion? 2.3 Why Recursion? 2.4 Format of a Recursive Function 2.5 Recursion and Memory (Visualization) 2.6 Recursion versus Iteration 2.7 Notes on Recursion 2.8 Example Algorithms of Recursion 2.9 Recursion: Problems & Solutions 2.10 What is Backtracking? 2.11 Example Algorithms of Backtracking 2.12 Backtracking: Problems & Solutions 3. Linked Lists 3.1 What is a Linked List? 3.2 Linked Lists ADT 3.3 Why Linked Lists? 3.4 Arrays Overview 3.5 Comparison of Linked Lists with Arrays & Dynamic Arrays 3.6 Singly Linked Lists 3.7 Doubly Linked Lists 3.8 Circular Linked Lists 3.9 A Memory-efficient Doubly Linked List 3.10 Unrolled Linked Lists 3.11 Skip Lists 3.12 Linked Lists: Problems & Solutions 4. Stacks 4.1 What is a Stack? 4.2 How Stacks are used 4.3 Stack ADT 4.4 Applications 4.5 Implementation 4.6 Comparison of Implementations 4.7 Stacks: Problems & Solutions 5. Queues 5.1 What is a Queue? 5.2 How are Queues Used? 5.3 Queue ADT 5.4 Exceptions 5.5 Applications 5.6 Implementation 5.7 Queues: Problems & Solutions 6. Trees 6.1 What is a Tree? 6.2 Glossary 6.3 Binary Trees 6.4 Types of Binary Trees 6.5 Properties of Binary Trees 6.6 Binary Tree Traversals 6.7 Generic Trees (N-ary Trees) 6.8 Threaded Binary Tree Traversals (Stack or Queue-less Traversals) 6.9 Expression Trees 6.10 XOR Trees 6.11 Binary Search Trees (BSTs) 6.12 Balanced Binary Search Trees 6.13 AVL(Adelson-Velskii and Landis) Trees 6.14 Other Variations on Trees 7. Priority Queues and Heaps 7.1 What is a Priority Queue? 7.2 Priority Queue ADT 7.3 Priority Queue Applications 7.4 Priority Queue Implementations 7.5 Heaps and Binary Heaps 7.6 Binary Heaps 7.7 Heapsort 7.8 Priority Queues [Heaps]: Problems & Solutions 8. Disjoint Sets ADT 8.1 Introduction 8.2 Equivalence Relations and Equivalence Classes 8.3 Disjoint Sets ADT 8.4 Applications 8.5 Tradeoffs in Implementing Disjoint Sets ADT 8.8 Fast UNION Implementation (Slow FIND) 8.9 Fast UNION Implementations (Quick FIND) 8.10 Summary 8.11 Disjoint Sets: Problems & Solutions 9. Graph Algorithms 9.1 Introduction 9.2 Glossary 9.3 Applications of Graphs 9.4 Graph Representation 9.5 Graph Traversals 9.6 Topological Sort 9.7 Shortest Path Algorithms 9.8 Minimal Spanning Tree 9.9 Graph Algorithms: Problems & Solutions 10. Sorting 10.1 What is Sorting? 10.2 Why is Sorting Necessary? 10.3 Classification of Sorting Algorithms 10.4 Other Classifications 10.5 Bubble Sort 10.6 Selection Sort 10.7 Insertion Sort 10.8 Shell Sort 10.9 Merge Sort 10.10 Heap Sort 10.11 Quick Sort 10.12 Tree Sort 10.13 Comparison of Sorting Algorithms 10.14 Linear Sorting Algorithms 10.15 Counting Sort 10.16 Bucket Sort (or Bin Sort) 10.17 Radix Sort 10.18 Topological Sort 10.19 External Sorting 10.20 Sorting: Problems & Solutions 11. Searching 11.1 What is Searching? 11.2 Why do we need Searching? 11.3 Types of Searching 11.4 Unordered Linear Search 11.5 Sorted/Ordered Linear Search 11.6 Binary Search 11.7 Interpolation Search 11.8 Comparing Basic Searching Algorithms 11.9 Symbol Tables and Hashing 11.10 String Searching Algorithms 11.11 Searching: Problems & Solutions 12. Selection Algorithms [Medians] 12.1 What are Selection Algorithms? 12.2 Selection by Sorting 12.3 Partition-based Selection Algorithm 12.4 Linear Selection Algorithm – Median of Medians Algorithm 12.5 Finding the K Smallest Elements in Sorted Order 12.6 Selection Algorithms: Problems & Solutions 13. Symbol Tables 13.1 Introduction 13.2 What are Symbol Tables? 13.3 Symbol Table Implementations 13.4 Comparison Table of Symbols for Implementations 14. Hashing 14.1 What is Hashing? 14.2 Why Hashing? 14.3 HashTable ADT 14.4 Understanding Hashing 14.5 Components of Hashing 14.6 Hash Table 14.7 Hash Function 14.8 Load Factor 14.9 Collisions 14.10 Collision Resolution Techniques 14.11 Separate Chaining 14.12 Open Addressing 14.13 Comparison of Collision Resolution Techniques 14.14 How Hashing Gets O(1) Complexity? 14.15 Hashing Techniques 14.16 Problems for which Hash Tables are not suitable 14.17 Bloom Filters 14.18 Hashing: Problems & Solutions 15. String Algorithms 15.1 Introduction 15.2 String Matching Algorithms 15.3 Brute Force Method 15.4 Rabin-Karp String Matching Algorithm 15.5 String Matching with Finite Automata 15.6 KMP Algorithm 15.7 Boyer-Moore Algorithm 15.8 Data Structures for Storing Strings 15.9 Hash Tables for Strings 15.10 Binary Search Trees for Strings 15.11 Tries 15.12 Ternary Search Trees 15.13 Comparing BSTs, Tries and TSTs 15.14 Suffix Trees 15.15 String Algorithms: Problems & Solutions 16. Algorithms Design Techniques 16.1 Introduction 16.2 Classification 16.3 Classification by Implementation Method 16.4 Classification by Design Method 16.5 Other Classifications 17. Greedy Algorithms 17.1 Introduction 17.2 Greedy Strategy 17.3 Elements of Greedy Algorithms 17.4 Does Greedy Always Work? 17.5 Advantages and Disadvantages of Greedy Method 17.6 Greedy Applications 17.7 Understanding Greedy Technique 17.8 Greedy Algorithms: Problems & Solutions 18. Divide and Conquer Algorithms 18.1 Introduction 18.2 What is the Divide and Conquer Strategy? 18.3 Does Divide and Conquer Always Work? 18.4 Divide and Conquer Visualization 18.5 Understanding Divide and Conquer 18.6 Advantages of Divide and Conquer 18.7 Disadvantages of Divide and Conquer 18.8 Master Theorem 18.9 Divide and Conquer Applications 18.10 Divide and Conquer: Problems & Solutions 19. Dynamic Programming 19.1 Introduction 19.2 What is Dynamic Programming Strategy? 19.3 Properties of Dynamic Programming Strategy 19.4 Can Dynamic Programming Solve All Problems? 19.5 Dynamic Programming Approaches 19.6 Examples of Dynamic Programming Algorithms 19.7 Understanding Dynamic Programming 19.8 Longest Common Subsequence 19.9 Dynamic Programming: Problems & Solutions 20. Complexity Classes 20.1 Introduction 20.2 Polynomial/Exponential Time 20.3 What is a Decision Problem? 20.4 Decision Procedure 20.5 What is a Complexity Class? 20.6 Types of Complexity Classes 20.7 Reductions 20.8 Complexity Classes: Problems & Solutions 21. Miscellaneous Concepts 21.1 Introduction 21.2 Hacks on Bit-wise Programming 21.3 Other Programming Questions

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