# 7.3 Analyzing Procedures

By considering the asymptotic growth of functions, rather than their actual outputs, the $O$, $\Omega$, and $\Theta$ operators allow us to hide constants and factors that change depending on the speed of our processor, how data is arranged in memory, and the specifics of how our interpreter is implemented. Instead, we can consider the essential properties of how the running time of the procedures increases with the size of the input.

This section explains how to measure input sizes and running times. To understand the growth rate of a procedure's running time, we need a function that maps the size of the inputs to the procedure to the amount of time it takes to evaluate the application. First we consider how to measure the input size; then, we consider how to measure the running time. In Section 7.3.3 we consider *which* input of a given size should be used to reason about the cost of applying a procedure. Section 7.4 provides examples of procedures with different growth rates. The growth rate of a procedure's running time gives us an understanding of how the running time increases as the size of the input increases.