expected linear time

Expected linear time refers to an average-case scenario where an algorithm's running time grows proportionally with the size of the input. For example, if you double the amount of data, the time it takes to process it roughly doubles as well. This means the algorithm is efficient and predictable when handling larger data sets, as its performance increases at a steady, linear rate rather than exponentially or unpredictably. It's based on expected or average performance across various possible inputs, assuming typical conditions.