Which of the following Big O notations is appropriate for the complexity of a search algorithm?(1 point) Responses O(n2) upper O left parenthesis n squared right parenthesis O(logn) upper O left parenthesis log n right parenthesis O(n) upper O left parenthesis n right parenthesis O(1)

Answer 1

The appropriate Big O notation for the complexity of a search algorithm can vary based on the specific algorithm used. Here are some common cases:

1. O(1): This is the best case, indicating constant time complexity (e.g., accessing an element in a hash table by its key).

2. O(log n): This is typical for binary search algorithms on sorted data.

3. O(n): For linear search algorithms, which search through a list or array sequentially.

4. O(n²): This is generally not suitable for search algorithms as it indicates a quadratic complexity, which is more common for algorithms like bubble sort or other comparison-based sorting techniques.

In summary, the appropriate Big O notations for a search algorithm could be O(1), O(log n), or O(n), depending on the particular search algorithm being referenced.

If you need to choose the most efficient among the options provided, O(log n) would often be seen as indicative of a more efficient searching algorithm like binary search, while O(n) is a good general case for linear search.

Given your question, the correct responses could be:

• O(log n) for binary search.
• O(n) for linear search.
• O(1) if you are using a hash table.

Choose any of these options based on the context of your question. If you need to select one, O(log n) for binary search is a widely recognized efficient complexity for search operations.