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2 cards from this section. Quick, good the night before a test.
What is time complexity?
Think about how we measure performance over different inputs.
Explain what a step is in algorithm analysis.
Consider fundamental operations in programming.
What does time complexity measure?
Think about the steps defined in algorithm analysis.
True or False: O(n^2) algorithms are better than O(n log n) algorithms for large datasets.
Consider how each function grows as the input size increases.
An algorithm with time complexity O(n^2) is used to handle input sizes varying from 100 to 1000. Discuss the predicted running times at 100, 500, and 1000. Compare it with an O(n log n) algorithm for the same input sizes.
Use the definitions of complexities to calculate the steps.
Describe the real-world consequences of using O(n^3) algorithms in software applications designed for real-time processing.
Consider scenarios where response time is critical.
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