Algorithms Analysis Practice Test 2026 – All-in-One Mastery Guide to Exam Success

Algorithms classified under quadratic complexity have a time complexity of O(n^2). This means that the time required to complete the algorithm increases quadratically as the size of the input (n) increases. In practical terms, if you were to double the size of the input, the time taken by the algorithm would increase by a factor of four, illustrating how the performance degrades rapidly with larger inputs.

Quadratic time complexities commonly arise in algorithms that involve nested iterations over the data set, such as bubble sort or selection sort, where each element is compared with every other element. This results in a total of approximately n * n or n^2 operations.

The other provided options represent different types of time complexities. For instance, linear time complexity (O(n)) indicates that the processing time increases in direct proportion to the input size, while logarithmic time complexity (O(log n)) suggests that the processing time grows much slower compared to the input size. Finally, O(n log n) denotes a linearithmic complexity, often seen in more efficient sorting algorithms like mergesort or heapsort, which perform better than quadratic but worse than linear complexities. Therefore, O(n^2) accurately describes the behavior of algorithms with quadratic complexity.