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4 cards from this section. Quick, good the night before a test.
What does time complexity measure?
Think about the relationship between time and input size.
Give an example of an algorithm with O(n) complexity.
Consider how many comparisons you would make in the worst case.
What does 'O' in O(n) represent?
Focus on the concept of how we describe algorithm efficiency.
True or False: A sorting algorithm with O(n²) will always be faster than one with O(n log n) for large datasets.
Think about the efficiency relative to increasing dataset sizes.
Design an algorithm for sorting a dataset of 100 million entries. Choose between a naive approach and an efficient one, justify your choice.
Consider the time each algorithm would take based on their complexities.
Create a scenario where using an exponential algorithm is acceptable. Describe its limitations.
Think about scenarios in theoretical contexts versus practical applications.
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