The Fundamentals of Algorithm Design and Analysis is a key topic in computer science and software engineering that focuses on finding optimal methods for solving computational problems. This course first examines the concept of an algorithm and its characteristics, such as correctness, efficiency, simplicity, and optimality. Then, various techniques for designing efficient algorithms are studied.
Main Topics Covered in This Course:
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Algorithm Performance Analysis:
- Methods for evaluating time complexity and determining asymptotic notations (Big-O, Theta, Omega).
- Space complexity analysis to measure the memory usage of algorithms.
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Algorithm Design Techniques:
- Divide and Conquer: Breaking a problem into smaller subproblems and solving them recursively ( QuickSort, MergeSort).
- Dynamic Programming: Using previously computed results to solve overlapping subproblems efficiently ( Knapsack problem, Floyd-Warshall).
- Greedy Algorithms: Making step-by-step local optimal choices to reach a global solution ( Kruskal’s algorithm, Huffman coding).
- Backtracking and Branch & Bound: Techniques for exploring all possible solutions to combinatorial problems.
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Classic Algorithms and Practical Problems:
- Searching and Sorting ( QuickSort, MergeSort, Binary Search).
- Graph Algorithms ( Dijkstra’s Algorithm, BFS, DFS).
- Computational Complexity and NP Problems ( NP-Complete, P vs NP).
More info at http://sharif.edu/~ghodsi/?page=da-alg-book
can get it at https://book.sharif.ir/user/getDocInfo/442