As programmers, one of our main jobs is to reason about algorithms and put them to use. And one of the tools we use for this task is complexity analysis. Measured in “Big O” notation, an algorithm’s complexity gives us a rough idea of how quickly it performs in the face of different input sizes. For example, we learn that inserting an item into the middle of a linked list is a constant time (O(1)) operation, while inserting into the middle of an array is done in linear time (O(n)), since we must...