What does algorithm complexity measure?

Algorithm complexity primarily measures the efficiency of an algorithm in terms of both time and space resources. Time complexity evaluates how the runtime of an algorithm increases with the size of the input data, providing insights into how quickly an algorithm can execute as the dataset grows. Space complexity, on the other hand, assesses how much memory an algorithm requires in relation to the input size. The overall goal of understanding algorithm complexity is to predict and optimize resource usage, making it crucial for developing efficient programs, especially when dealing with large datasets or limited computing resources.

The focus on time and space allows developers to choose the most appropriate algorithms depending on their efficiency needs for specific applications. An algorithm that performs efficiently in one context might not necessarily do so in another, thus knowing the complexity helps in making informed decisions about which algorithms to implement.