When analyzing an algorithm, we generally want the algorithm with the lowest time complexity, as this means it will run faster.

Here are the options ranked from best (fastest) to worst (slowest):

1. O(log n)
2. O(n log n)
3. O(100 log n)
4. O(n!)

So, the best computing time among these options would be O(log n). This is because logarithmic time complexity grows the slowest as the size of the input increases.

Note: O(100 log n) and O(log n) are technically the same in terms of time complexity (since we ignore constants when discussing Big O notation), but in practical terms, an algorithm that takes 100 times longer to run would be less efficient.

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When analyzing an algorithm, we generally want the algorithm with the lowest time complexity, as this means it will run faster.

Here are the options ranked from best (fastest) to worst (slowest):

1. O(log n)
2. O(n log n)
3. O(100 log n)
4. O(n!)

So, the best computing time among these options would be O(log n). This is because logarithmic time complexity grows the slowest as the size of the input increases.

Note: O(100 log n) and O(log n) are technically the same in terms of time complexity (since we ignore constants when discussing Big O notation), but in practical terms, an algorithm that takes 100 times longer to run would be less efficient.

Knowee AI · Accepted Answer

When analyzing an algorithm, we generally want the algorithm with the lowest time complexity, as this means it will run faster.

Here are the options ranked from best (fastest) to worst (slowest):

1. O(log n)
2. O(n log n)
3. O(100 log n)
4. O(n!)

So, the best computing time among these options would be O(log n). This is because logarithmic time complexity grows the slowest as the size of the input increases.

Note: O(100 log n) and O(log n) are technically the same in terms of time complexity (since we ignore constants when discussing Big O notation), but in practical terms, an algorithm that takes 100 times longer to run would be less efficient.

or analyzing an algorithm which is better computing time?Question 2Answera.О(nlogn)b.Оc.О(100 log n)d.О(n!)

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