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Comprehensive software engineering curriculum
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Module 5
Algorithmic Design Paradigms
1
Binary Search: Implementation and Complexity Analysis
Implement binary search on a sorted array and analyze its logarithmic complexity.
2
Divide and Conquer: Mergesort & Quicksort
Explain the Divide and Conquer paradigm using Mergesort or Quicksort as a primary example.
3
Greedy Algorithms: Principles and Conditions
Explain the Greedy paradigm and the conditions for its applicability (greedy-choice property, optimal substructure).
Greedy Algorithms for Optimization
Apply a greedy algorithm to solve a classic optimization problem (e.g., activity selection, fractional knapsack).
5
Recognizing DP Opportunities
Identify problems with optimal substructure and overlapping subproblems, indicating suitability for Dynamic Programming.
6
Algorithmic Paradigms: A Comparative Analysis
Compare and contrast the Divide and Conquer, Greedy, and Dynamic Programming paradigms.
7
Quicksort: In-Place Partitioning and Performance Analysis
Implement Quicksort with an in-place partitioning scheme and analyze its average vs. worst-case performance.
Previous module
Core Graph Algorithms
Next module
Dynamic Programming in Practice