O(n) represents time complexity and linear order. With the increase of the problem scale n, the time complexity increases and the efficiency of algorithm execution decreases.

According to the order of magnitude, the common time complexity is:

Constant order O( 1), logarithmic order O(log2n), linear order O(n), linear logarithmic order O(nlog2n), square order o (n 2), cubic order o (n 3), ..., k power o (n k), exponential power o (2 n).

Extended data:

In computer science, the time complexity of an algorithm is a function, which qualitatively describes the running time of the algorithm. This is a function of the length of the string representing the input value of the algorithm. Time complexity is often represented by a big O symbol, excluding the low-order term and the first term coefficient of the function.

The algorithm complexity is divided into time complexity and space complexity. Its function: time complexity refers to the calculation workload required to execute the algorithm; Spatial complexity refers to the memory space needed to execute the algorithm. The complexity of the algorithm is reflected in the amount of resources needed by the computer to run the algorithm. The most important computer resources are time and space (that is, registers) resources, so complexity is divided into time complexity and space complexity.

References:

Baidu encyclopedia-time complexity