Algorithms Analysis Practice Test 2025 – All-in-One Mastery Guide to Exam Success

The representation of quadratic growth indicates that the running time of the algorithm increases proportionally to the square of the input size. In big-O notation, quadratic growth is specifically denoted as O(n²). This means that if the input size doubles, the running time increases by a factor of four (since \( (2n)² = 4n² \)).

This growth pattern reflects how the algorithm's performance deteriorates as the input size becomes larger, making it important to recognize that O(n²) captures this specific rate of growth accurately. Understanding big-O notation helps in comparing the efficiency of different algorithms, especially when analyzing their performances as the input size scales.

Other options such as O(n), O(n log n), and O(2^n) represent different rates of growth. O(n) indicates linear growth, O(n log n) suggests a combination of linear and logarithmic growth, and O(2^n) signifies exponential growth, none of which match the quadratic growth described in the question. Therefore, O(n²) is the suitable choice for an algorithm demonstrating quadratic growth.