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4 cards from this section. Quick, good the night before a test.
What is the purpose of proving an algorithm's correctness?
Consider what makes an algorithm reliable.
Define asymptotic complexity in relation to algorithm efficiency.
Think about how we compare algorithms.
What does the Big O notation represent?
Remember what we want to compare when looking at algorithms.
True or False: A greedy algorithm always finds the global optimal solution.
Consider examples where a greedy choice might backfire.
Consider a problem where you have a list of numbers and need to find the median. Design an algorithm using both sorting and a divide and conquer approach, compare their efficiencies.
Which algorithm would you prefer if you were given a much larger dataset?
You need to design a web service that responds to a user query in less than a second. Analyze how your choice of data structure could affect your service's response time. Discuss alternatives if your initial choice is not optimized enough.
What happens when your dataset grows very large?
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