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Explain the primary purpose of searching algorithms in computer science.
Describe how the binary search algorithm works and its time complexity.
What is the main advantage of using sorting algorithms in computer science?
Compare the time complexities of bubble sort and merge sort.
Why is it important to understand the trade-offs between different sorting algorithms?
Explain how a linear search algorithm works and in what scenarios it is most useful.
Describe how a binary search algorithm works and why it is more efficient than a linear search for large, sorted lists.
What are the prerequisites for using a binary search algorithm, and why are they necessary?
In what situations would a linear search be preferred over a binary search, despite its lower efficiency?
What is the main advantage of using a binary search over a linear search, and what is a key limitation?
Explain how the quicksort algorithm works and what its average-case time complexity is.
Describe the merge sort algorithm and its space complexity.
What is the insertion sort algorithm, and in what scenario is it most efficient?
How does the selection sort algorithm work, and what is its primary disadvantage?
What is the heap sort algorithm, and what is its time complexity?
Compare the time complexities of bubble sort and merge sort.
(Student response here)
Bubble sort has a time complexity of in both its average and worst cases, making it inefficient for large datasets. It repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. Merge sort, on the other hand, has a time complexity of in all cases. It is a divide-and-conquer algorithm that divides the list into smaller sublists, sorts them, and then merges them back together. Merge sort is generally more efficient than bubble sort for large datasets.
Which sorting algorithm has a better time complexity for large datasets?