Algorithm Design Paradigms and Amortised Analysis
Students will understand abstraction as simplifying complex ideas by focusing on essential details and hiding unnecessary ones.
About This Topic
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.
Active Learning Ideas
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