Which of the following Big O notations is appropriate for the complexity of a search algorithm?(1 point)

Question

Responses

O(n)
upper O left parenthesis n right parenthesis

O(logn)
upper O left parenthesis log n right parenthesis

O(1)
upper O left parenthesis 1 right parenthesis

O(n2)

Answer 1

The appropriate Big O notation for the complexity of a search algorithm can vary depending on the type of search algorithm being utilized. Here are common scenarios:

1. O(n) - This typically applies to a linear search algorithm, where you need to check each element in a list one by one.

2. O(log n) - This applies to more efficient searching algorithms like binary search, which requires the data to be sorted and repeatedly divides the search interval in half.

3. O(1) - This is indicative of a constant time search, which is not common in general search algorithms but can apply in situations where you know exactly where the item is (like searching in a hash table with a perfect hash function).

4. O(n²) - This usually pertains to algorithms that involve nested iterations over the data, such as searching within a 2D list in a brute-force manner.

Given these definitions, the most appropriate choices would generally be O(n) for linear search and O(log n) for binary search. But without more context, if we are to choose the best general answer for a search algorithm, O(log n) (binary search) would be a preferred optimal efficiency in sorted data scenarios.

If you need to specify the best answer: O(log n) would be most suitable when considering efficiency in searching a sorted dataset. If the dataset is unsorted, then O(n) would apply for a linear search.

Answer 2

so whats the answer

Answer 3

The answer depends on the context of the search algorithm being referenced:

• If you are referring to a linear search algorithm in an unsorted list, the answer is O(n).
• If you are referring to a binary search algorithm in a sorted list, the answer is O(log n).
• If you know the exact location of the data (for example, a hash map), you might use O(1).
• O(n²) is generally not relevant for typical search algorithms.

Given that you're looking for the general complexity of a search algorithm, and considering the most efficient common case (binary search on sorted data), the correct answer would be O(log n).