The Importance of Learning Algorithms Before introducing specific algorithms, let me talk about my initial intention of learning algorithms.
My initial intention is nothing more than two points: First, Internet companies such as BAT should require algorithmic knowledge when recruiting interviews. If I want to enter an internet company, I must learn the algorithm well; Secondly, learning algorithms can improve the basic skills of personal development, so that I can correctly choose the corresponding data structure and algorithm for different scenarios, make the program more robust and improve the running efficiency of the program.
Application fields At present, every subdivision field of computer involves different algorithms.
For example, in search engines, we usually use browsers such as Google and Baidu. As long as we enter a keyword, the browser will quickly return the relevant collection, and there are many algorithms hidden behind this collection.
Without these algorithms, we can't get the desired results so quickly.
Another example is artificial intelligence, which realizes various application scenarios such as human body recognition and voice recognition through computational model algorithms.
Algorithm analysis As mentioned above, an algorithm is a solution to a problem, and there may be different solutions to the same problem.
Therefore, in order to measure the advantages and disadvantages of an algorithm, the concepts of time complexity and space complexity are proposed.
Time Complexity Generally speaking, the number of repetitions of basic operations in the algorithm is a function of the problem scale n, and the time metric of the algorithm is recorded as t (n) = o (f(n)), which means that with the increase of the problem scale n, the growth rate of the algorithm execution time is the same as that of f(n), which is called the asymptotic time complexity of the algorithm, or time complexity for short.
Spatial Complexity Spatial complexity is a measure of the temporary storage space occupied by the algorithm in the running process, which is expressed by S(n)=O(f(n)).
The advantages and disadvantages of an algorithm are mainly measured from two aspects: the execution time of the algorithm and the required storage space.