Understanding Algorithm Complexity

What does Big O Notation represent in algorithm analysis?

• Big O Notation represents the upper bound of an algorithm's time complexity.

• Big O Notation measures the average case of an algorithm's space complexity.

• Big O Notation is used to compare the efficiency of different programming languages.

• Big O Notation indicates the exact runtime of an algorithm.

Which of the following is the best case time complexity for a linear search algorithm?

• O(log n)

• O(1)

• O(n^2)

• O(n)

What is the time complexity of binary search in a sorted array?

• O(n log n)

• O(1)

• O(log n)

• O(n)

How does space complexity differ from time complexity?

• Space complexity is irrelevant to algorithm performance, while time complexity is the only factor that matters.

• Space complexity focuses on memory usage, while time complexity focuses on execution time.

• Time complexity is concerned with the number of operations, while space complexity is about the speed of execution.

• Space complexity measures algorithm efficiency, while time complexity measures memory usage.

What is the Big O notation for an algorithm that runs in constant time?

• O(n^2)

• O(log n)

• O(n)

• O(1)

In the worst case scenario, what is the time complexity of bubble sort?

• O(n^2)

• O(log n)

• O(n log n)

• O(n)

What is the best case time complexity for quicksort?

• O(log n)

• O(n log n)

• O(n^2)

• O(n)

How can you improve the efficiency of an algorithm?

• Increase the number of operations

• Analyze and optimize time and space complexity, use efficient algorithms, and reduce operations.

• Use outdated algorithms

• Ignore time complexity

What is the space complexity of an algorithm that uses a fixed number of variables?

• O(1)

• O(log n)

• O(n)

• O(n^2)

What is the worst case time complexity for a hash table lookup?

• O(n)

• O(log n)

• O(1)

• O(n log n)