Western Governors University (WGU) ICSC2100 C949 Data Structures and Algorithms I Practice Exam

Big O notation is a mathematical concept used to describe the upper bound of an algorithm's performance in terms of time complexity. It specifically aids in characterizing the worst-case scenario for an algorithm as the input size increases, allowing developers and computer scientists to understand the maximum amount of time and resources an algorithm might require.

By utilizing Big O notation, one can assess how efficiently an algorithm performs under the least favorable conditions. This understanding is crucial for evaluating algorithms, especially when choosing the best one for a particular problem or when ensuring that the algorithm can handle large-scale data without significant slowdowns.

For example, if an algorithm runs in O(n^2) time complexity, this means that as the input size (n) doubles, the time taken to execute the algorithm could quadruple in the worst-case scenario. This insight is pivotal when analyzing algorithm performance, particularly for scenarios requiring optimal run times under increasing input sizes.