This course provides a comprehensive understanding of algorithm design and analysis, focusing on evaluating the efficiency and applicability of algorithms for solving real-world problems. Students begin by learning asymptotic notations to analyze time and space complexity, enabling them to compare algorithmic performance effectively. The course explores fundamental algorithm design paradigms such as divide and conquer, greedy methods, and dynamic programming for developing efficient solutions. Advanced problem-solving techniques including backtracking and branch and bound are covered to address complex computational problems. Students are introduced to NP-complete problems and learn to predict their computational complexity using appropriate algorithmic strategies. The course also emphasizes practical implementation of classical graph and optimization algorithms such as Dijkstra’s, Bellman–Ford, Prim’s, and Kruskal’s, along with pattern matching techniques like Rabin–Karp and brute-force methods, to solve real-world applications including routing, scheduling, and searching problems.
- Teacher: Kamal kumar Gola