Algorithm Complexity Analysis: Big-O and Case Analysis
Students will use flowcharts to visually represent the steps and decisions in an algorithm before writing code.
About This Topic
Students will use flowcharts to visually represent the steps and decisions in an algorithm before writing code.
Key Questions
- Derive the time complexity of algorithms involving nested loops, recursive calls, and loop-dependent bounds, expressing results precisely using Big-O, Big-Omega, and Big-Theta notation.
- Apply the master theorem to determine the time complexity of divide-and-conquer recurrences such as merge sort and binary search, and identify the conditions under which each case of the theorem applies.
- Analyse the space complexity of a recursive algorithm versus its iterative equivalent, explaining the role of the call stack and identifying scenarios where the recursive solution is inadvisable despite equivalent time complexity.
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