If an algorithm's running time grows proportionally to the square of its input size (n), how would its time complexity be formally expressed using Big O notation?

If an algorithm's running time grows proportionally to the square of its input size (n), its time complexity is formally expressed using Big O notation as O(n^2). Big O notation is a mathematical tool used to describe the asymptotic upper bound of an algorithm's running time or space requirements. It characterizes how the algorithm's performance scales as the input size, denoted by n, approaches infinity, focusing on the worst-case scenario or the maximum amount of time an algorithm might take. Time complexity refers to the total number of elementary operations an algorithm performs, such as comparisons, assignments, or arithmetic calculations, as a function of its input size. Input size (n) represents the number of elements, items, or the magnitude of data that an algorithm processes. For instance, in a sorting algorithm, n could be the number of items in a list. When an algorithm's running time 'grows proportionally to the square of its input size (n)', it means that the number of operations performed by the algorithm will be roughly proportional to n multiplied by itself (n n, or n^2). This implies that if the input size doubles, the running time approximately quadruples. For example, an algorithm processing 10 items might take around 100 units of time, while processing 20 items would take roughly 400 units of time. In Big O notation, constant factors and any lower-order terms (like n or constant terms) are disregarded because their impact becomes insignificant compared to the dominant term, n^2, as n becomes very large. Since the algorithm's running time growth is fundamentally determined by the n^2 term, O(n^2) accurately represents this quadratic relationship, providing a clear upper bound on the algorithm's performance and indicating its quadratic scalability with respect to its input size.