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5 cards from this section. Quick, good the night before a test.
What is the main focus of algorithm analysis?
Think about what we need to evaluate in an algorithm's performance.
Define 'step' in the context of algorithms.
What actions in programming might be considered a step?
What is the primary measure of an algorithm's efficiency?
Consider the main goal of analyzing algorithms.
True or False: Space complexity is more critical than time complexity when analyzing algorithm efficiency.
Think about which resources are generally harder to adjust in a computing scenario.
Design and analyze an algorithm for finding the maximum number in a list of 10,000 integers. What are its time and space complexities?
Consider how you would iterate through the list and keep track of the maximum value.
Discuss the implications of using an O(n²) bubble sort on a dataset of size 1,000,000 versus using an O(n log n) merge sort. What are the practical outcomes?
Use growth rates to compare the expected operations for both algorithms and their respective input sizes.
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