If an algorithm has a time complexity of O(n log n), what can you infer about its runtime as the input size doubles?
It increases exponentially
It remains constant
It increases slightly more than double
It increases by a factor of log n
What is the time complexity of the QuickSort algorithm in the worst-case scenario?
O(n^2)
O(n log n)
O(n)
O(log n)
What is the worst-case time complexity of the linear search algorithm?
O(1)
Which data structure, when used for searching, can potentially improve the time complexity from O(n) to O(log n)?
Queue
Linked List
Array
Binary Tree
Which of these Big-O notations represents the most efficient algorithm for large input sizes?
What is the best-case time complexity of the insertion sort algorithm?
Why is understanding time complexity crucial in algorithm analysis?
To determine the exact execution time of an algorithm
To calculate the cost of developing an algorithm
To compare the aesthetic quality of different algorithms
To predict how the performance of an algorithm scales with larger inputs
You have two algorithms for a task: Algorithm A has a time complexity of O(n log n), and Algorithm B has O(n^2). For which input size 'n' would Algorithm A likely start to outperform Algorithm B?
n = 10
It depends on the specific algorithms and their constant factors.
n = 1000
n = 100
Why is it crucial to consider real-world performance alongside theoretical time complexity analysis when designing algorithms?
Real-world factors like hardware and data distribution can significantly impact performance.
Theoretical analysis is only relevant for academic purposes.
Theoretical analysis is sufficient for predicting real-world performance.
Real-world performance is always better than theoretical predictions.
In what scenario might an algorithm with a worse theoretical time complexity perform better in practice than one with a better complexity?
All of the above.
When the input data size is very small.
When the algorithm with better complexity has a very large constant factor hidden in its Big O notation.
When the algorithm with worse complexity is implemented in a more efficient programming language.