Design & Analysis of Algorithm (DAA) - The Smart Notes
Introduction to DAA
**Design and Analysis of Algorithm (DAA)** is a fundamental subject in computer science that focuses on designing efficient algorithms and evaluating their performance. It involves analyzing an algorithm's **time complexity, space complexity, and optimization techniques** to improve computational efficiency. Understanding DAA is essential for problem-solving and competitive programming.
Key Features of DAA
- Time Complexity: Measures algorithm efficiency using Big-O, Theta (Θ), and Omega (Ω) notations.
- Sorting Algorithms: QuickSort, MergeSort, BubbleSort, and HeapSort for data organization.
- Graph Algorithms: Includes Dijkstra’s Algorithm, Floyd-Warshall Algorithm, and Prim’s Algorithm.
- Dynamic Programming: Solves problems like Fibonacci series, Knapsack, and Matrix Chain Multiplication.
- Greedy Algorithms: Solves optimization problems like Huffman Encoding and Kruskal’s Algorithm.
- Divide & Conquer: Used in Merge Sort, Quick Sort, and Binary Search.
- NP-Complete & NP-Hard Problems: Understanding computational complexity classes.
Applications of DAA
DAA is widely used in various fields, including:
- Software Development: Optimizing applications for better performance.
- Artificial Intelligence: Implementing AI algorithms with efficient search techniques.
- Database Indexing: Improving data retrieval efficiency in large datasets.
- Network Optimization: Routing algorithms used in networking.
- Game Development: Pathfinding and decision-making algorithms.