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MSC Semester I - DESIGN AND ANALYSIS OF ALGORITHMS
Updated on Sep, 2026
Programming, Data Science, Data Analysis
Duration - 17.5 hours
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UNIT – I: INTRODUCTION TO ALGORITHMS Algorithm Specification; Performance Analysis; Randomized Algorithms. Elementary Data Structures: Stacks and Queues; Trees; Dictionaries; Priority Queues; Sets and Disjoint Set Union; Graphs. Divide and Conquer: Binary Search; Finding the Maximum and Minimum; Merge Sort; Quick Sort; Selection Sort; Strassen's Matrix Multiplication; Convex Hull. UNIT – II: GREEDY METHOD AND DYNAMIC PROGRAMMING Greedy Method: Knapsack Problem; Job Sequencing with Deadlines; Minimum-Cost Spanning Trees (Kruskal's & Prim's); Single Source Shortest Paths (Dijkstra's). Dynamic Programming: General Method; Multistage Graphs; All-Pairs Shortest Paths; Single-Source Shortest Paths; Optimal Binary Search Trees; 0/1 Knapsack; Travelling Salesperson Problem. UNIT – III: TRAVERSAL TECHNIQUES, BACKTRACKING AND BRANCH-AND-BOUND Basic Traversal and Search Techniques: Techniques for Binary Trees; Techniques for Graphs; Connected Components and Spanning Trees; Biconnected Components; DFS. Backtracking: General Method; 8-Queens Problem; Sum of Subsets; Graph Colouring; Hamiltonian Cycles; Knapsack Problem. Branch-and-Bound: General Method; 0/1 Knapsack Problem; Travelling Salesperson Problem. UNIT – IV: NP-HARD AND NP-COMPLETE PROBLEMS Basic Concepts; Cook's Theorem; NP-Hard Graph Problems; NP-Hard Scheduling Problems; NP-Hard Code Generation; Some Simplified NP-Hard Problems.
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