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3 cards from this section. Quick, good the night before a test.
What does time complexity measure?
Consider the factors influencing execution time.
What does O(n^2) denote?
Think about how many comparisons might be made.
What is more limiting in measuring algorithm efficiency?
Think about how we measure the performance of algorithms.
True or False: O(n^2) can outperform O(n log n) for large datasets.
Think about the mathematical growth of these functions.
Consider an algorithm requiring O(n^2) operations. If n=2000, how many operations will it perform, and how feasible is it based on typical operational limits of 10^8 per second?
Calculate using the square of n.
If an algorithm is O(n log n) and you have an input size of 1 million, determine how many operations are expected and whether this can be efficiently executed.
Break down O(log n) using logarithmic properties for simplification.
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