Data Structures and Algorithms Made Easy

Data Structures and Algorithms Made Easy


Author:
Catherine Wilson
Published in: Princeton University Press
Release Year: 1997
ISBN: 978-8193-24527-9
Pages: 145
Edition: 1st
File Size: 17 MB
File Type: pdf
Language: English



Description of Data Structures and Algorithms Made Easy


Please hold on! I know many people typically do not read the Preface of a book. But I strongly recommend that you read this particular Preface. It is not the main objective of Data Structures and Algorithms Made Easybook to present you with the theorems and proofs on data  structures and algorithms. I have followed a pattern of improving the problem solutions with different complexities (for each problem, you will find multiple solutions with different, and reduced, complexities). Basically, it’s an enumeration of possible solutions. With this approach, even if you get a new question, it will show you a way to think about the possible solutions. You will find Data Structures and Algorithms Made Easy book useful for interview preparation, competitive exams preparation, and campus  interview preparations.
As a job seeker, if you read the complete book, I am sure you will be able to challenge the interviewers. If you read it as an instructor, it will help you to deliver lectures with an approach that is easy to follow, and as a result your students will appreciate the fact that they have opted for Computer Science / Information Technology as their degree. Data Structures and Algorithms Made Easy book is also useful for Engineering degree students and Masters degree students during  their academic preparations. In all the chapters you will see that there is more emphasis on problems and their analysis rather than on theory. In each chapter, you will first read about the basic required theory, which is then followed by a section on problem sets. 
In total, there are approximately 700 algorithmic problems, all with solutions. If you read the book as a student preparing for competitive exams for Computer Science / Information Technology, the content covers all the required topics in full detail. While writing Data Structures and Algorithms Made Easy book, my main focus was to help students who are preparing for these exams.  In all the chapters you will see more emphasis on problems and analysis rather than on theory. In each chapter, you will first see the basic required theory followed by various problems. For many problems, multiple solutions are provided with different levels of complexity. We start with the brute force solution and slowly move toward the best solution possible for that problem. For each problem, we endeavor to understand how much time the algorithm takes and how much memory the algorithm uses.

Table Contents of Data Structures and Algorithms Made Easy


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 Simplyfying 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
References
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