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GATE CS 2026 Mastery Course
·
Module 8
Design & Analysis of Algorithms
1
Analyzing Algorithm Efficiency with the Master Theorem
Determine the asymptotic time complexity of algorithms and solve recurrence relations using the Master Theorem.
2
Sorting Algorithm Comparison
Compare the performance and stability of various sorting algorithms, including Merge Sort, Quicksort, and Heap Sort.
Non-Comparison Sorts: Counting & Radix
Implement and analyze non-comparison based sorting algorithms like Counting Sort and Radix Sort.
4
Divide and Conquer: Closest Pair and Integer Multiplication
Apply the divide-and-conquer paradigm to problems like finding the closest pair of points and integer multiplication.
5
Greedy Algorithms: Design and Proof of Correctness
Design greedy algorithms for problems like activity selection and fractional knapsack, and provide a proof of correctness.
6
Dynamic Programming for Classic Problems
Formulate dynamic programming solutions for classic problems like 0/1 Knapsack, LCS, and Matrix Chain Multiplication.
7
Graph Traversal: BFS and DFS Algorithms
Implement BFS and DFS to solve graph problems like finding connected components, topological sorting, and detecting cycles.
MST and Shortest Path Algorithms: Tracing Execution
Trace the execution of algorithms for finding Minimum Spanning Trees (Kruskal's, Prim's) and single-source shortest paths (Dijkstra's, Bellman-Ford).
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Data Structures
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Theory of Computation