Which of the following typically represents the most inefficient time complexity for large input sizes?
O(2^n)
O(n!)
O(n log n)
O(n^2)
Which notation represents a strict upper bound, meaning the function grows strictly slower than the specified function?
Little-omega (ω)
Big-O (O)
Little-o (o)
Big Theta (Θ)
Which notation is most useful when analyzing the average-case time complexity of an algorithm, considering all possible inputs?
All notations are equally useful for average-case analysis.
In the context of algorithm analysis, why are constant factors often ignored in asymptotic notations?
They are insignificant and have negligible impact on performance.
They are difficult to determine precisely and vary across different systems.
All of the above.
They become less relevant as the input size grows very large.
Merge sort and heapsort are examples of sorting algorithms with which time complexity?
O(n)
Which sorting algorithm is generally considered the fastest for large datasets with an average time complexity of O(n log n)?
Bubble Sort
Insertion Sort
Selection Sort
Merge Sort
What is the primary focus of Big-O notation in time complexity analysis?
Representing the lower bound of an algorithm's growth rate
Describing the upper bound of an algorithm's growth rate
Expressing the exact number of operations an algorithm performs
Calculating the average-case runtime of an algorithm
Which of the following statements is TRUE regarding the trade-off between code optimization and readability?
Code readability is irrelevant as long as the code achieves optimal performance.
There's no trade-off; optimal performance and readability always go hand-in-hand.
Highly optimized code is always easier to read and maintain.
Excessive optimization can sometimes hinder code readability, making maintenance difficult.
Which time complexity is characterized by an algorithm's runtime doubling with each additional input element?
Which time complexity is represented by an algorithm that iterates through a list of size n and performs a constant time operation in each iteration?
O(log n)
O(1)