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This article mainly explains "how to understand the complexity of algorithms," interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let Xiaobian take you to learn "how to understand the complexity of algorithms"!

1. Motivation -Why complexity analysis is needed

Post-event statistics (that is, running the code once, through statistics, monitoring to get the running time and occupied memory size) test results are very dependent on the test environment and the impact of data size is very large. But in fact, we need a method that can estimate the efficiency of the algorithm without specific test data.

2. large-O complexity notation

The efficiency of the algorithm is simply the execution time of the algorithm. For example, the execution time of the following code, assuming that the execution time of each line of code is the same, is unit_time. Based on this assumption, the total execution time of this code is (2n + 2)* unit_time.

int cal(int n) { int sum = 0; int i = 1; for (;i

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