PHP uses arrays to reduce the time complexity of programs.

This article mainly introduces how to use arrays to reduce the time complexity caused by multi-layer loops in PHP programming. Especially when the program needs to interact with the database multiple times, using this method to optimize your code will bring unexpected results. When writing a program, developers usually translate the operation logic that has been designed or conceived directly in programming languages. It is a pleasure that the program can be compiled smoothly. If the running time of the program is acceptable at this time, the program will be immersed in the sense of accomplishment of writing code, and the code optimization is often ignored in this process. Optimization is taken into consideration only when the program running speed is affected.

What is the time complexity of an algorithm?

Time complexity is the main factor that developers use to measure the merits and demerits of application algorithms. Objectively speaking, the advantages and disadvantages of algorithms are not only related to time complexity, but also closely related to space complexity. As the hardware configuration of devices continues to improve, the space complexity of algorithms is also relaxed for small and medium-sized applications. However, in today's Web era, the time complexity of applications has higher requirements.

What is the time complexity of the algorithm? In summary, it refers to selecting an original operation that can represent the algorithm from the algorithm, and measuring the number of times the original operation is repeated as the algorithm's time. There are two factors that affect the time complexity: one is the execution time of the original operation, and the other is the number of executions of the original operation due to the control structure. It is easier to reduce the time complexity of the algorithm and reduce the number of original operations. The method described in this article is to use PHP arrays to reduce the execution times of the original operation and reduce the time complexity of the algorithm.

The algorithm's time measurement is recorded as T (n) = O (f (n )), it indicates that the number of repeated executions of the basic operation in the algorithm is a function f (n) of the problem scale n. that is to say, as the problem scale n increases, the algorithm execution time increases and f (n) the growth rate is the same. In most cases, we take the statements in the deepest loop as the original operation to discuss the time complexity of the algorithm, because the execution frequency is the same as the frequency of the statements containing it. Generally, you only need to select a basic operation for a problem to discuss the time complexity of the algorithm. Sometimes you also need to consider multiple basic operations at the same time.

In Web development, the execution time or response time of a function is usually related not only to the server's response and processing capabilities, but also to the interaction time of third-party tools, for example, the connection time of the database and the access time of the data. Therefore, when selecting the original operation, you need to consider all aspects of the application, and take the operation that has the greatest impact on the execution time as the original operation to measure the time complexity of the algorithm. That is to say, programmers need to have a basic understanding of the execution time of important operations when writing code.