Algorithm Design Paradigms and Amortised Analysis
Students will understand abstraction as simplifying complex ideas by focusing on essential details and hiding unnecessary ones.
Key Questions
- Compare divide-and-conquer, greedy, and dynamic programming paradigms, providing a canonical problem for each and explaining why the other two paradigms fail or are suboptimal for that problem.
- Apply amortised analysis (aggregate, accounting, or potential method) to a dynamic array to explain why the amortised cost of append is O(1) despite occasional O(n) resizing operations.
- Evaluate whether a greedy algorithm yields an optimal solution for the activity-selection problem and the 0/1 knapsack problem, and explain the structural difference between these two problems that accounts for the different outcomes.
MOE Syllabus Outcomes
Suggested Methodologies
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