Detailed explanation of string matching algorithm implemented in Java

String matching algorithm is an important problem in computer science, it can be used to find the position of a string in another string. In practical application scenarios, string matching algorithms are often used in search engines, data mining, biological sequence analysis and other fields. This article will introduce in detail the commonly used string matching algorithms in Java and explore their advantages and disadvantages.

1. Brute-Force Algorithm

The Brute-Force Algorithm is the simplest and most basic algorithm among string matching algorithms. The idea is to start from the first character of the target string and match the first character of the pattern string. If the match is successful, continue matching backwards. Otherwise, it will trace back to the next character of the target string and continue matching with the first character of the pattern string. One character match. If the match is successful, repeat the above operation until all pattern strings are matched successfully, or all comparisons between the target string and the pattern string are completed.

The time complexity of the brute force matching algorithm is O(m*n), where m and n are the lengths of the target string and pattern string respectively. The brute force matching algorithm performs well when the length of the target string and the pattern string are not large. But when the length of the target string and pattern string gradually increases, the efficiency of the brute force matching algorithm will drop sharply because it will compare a large number of unnecessary characters.

2. KMP algorithm

KMP algorithm (Knuth-Morris-Pratt Algorithm) is a string matching algorithm that is more efficient than the brute force matching algorithm. The basic idea of ​​the KMP algorithm is to reduce unnecessary character comparisons with the help of already matched partial characters.

The KMP algorithm is mainly divided into two parts, preprocessing and matching. In the preprocessing stage, the KMP algorithm will build a longest prefix and suffix table of the pattern string, that is, the next array. In the matching stage, the KMP algorithm will use the next array to determine the movement position of the pattern string based on the longest prefix of the matched character and the suffix comparison of the matching position of the pattern string.

The time complexity of the KMP algorithm is O(m n), where m and n are the lengths of the target string and pattern string respectively. Compared with the brute force matching algorithm, the KMP algorithm uses preprocessing to make it perform better when matching a large amount of text. However, the KMP algorithm requires additional space to save the next array, so its space complexity is higher than that of the brute force matching algorithm.

3. BM algorithm

BM algorithm (Boyer-Moore Algorithm) is a string matching algorithm with small preprocessing calculation and high matching efficiency. The core idea of ​​the BM algorithm is to determine the position of the moved pattern string by considering the characters in the target string that match the last character of the unmatched part of the pattern string.

BM algorithm is divided into two stages, namely bad character rules and good suffix rules.

The bad character rule means that if a certain character in the target string does not match a character in the pattern string, the offset of the pattern string can be calculated based on the position where the bad character appears.

The good suffix rule means to find a suffix in the pattern string that matches the good suffix. If not, try to find if there is another suffix in the good suffix that matches it.

The time complexity of the BM algorithm is O(m n), where m and n are the lengths of the target string and pattern string respectively. Compared with KMP and brute force matching algorithms, the BM algorithm performs better in large string matching, but requires additional space to store the occurrence positions of characters in the pattern string.

4. Rabin-Karp algorithm

The Rabin-Karp algorithm is a hash-based string matching algorithm that calculates the hash value of each substring in constant time is calculated and compared with the hash value of the string to be matched.

The main idea of ​​the Rabin-Karp algorithm is to use the hash function to calculate the hash value of each substring in the target string, and then compare the hash value of the pattern string with the hash of each substring in the target string. values ​​are compared. Because the mapping of hash functions is unique, if the hash values ​​of the pattern string and a target string substring are equal, then there is a high probability that the two strings are equal.

The time complexity of the Rabin-Karp algorithm is O(m n), where m and n are the lengths of the target string and pattern string respectively. Compared with KMP and BM algorithms, the Rabin-Karp algorithm consumes less memory, but requires additional comparison operations when handling hash collisions.

To sum up, the commonly used string matching algorithms in Java mainly include brute force matching algorithm, KMP algorithm, BM algorithm and Rabin-Karp algorithm. These algorithms have their own advantages and disadvantages in implementation and performance, and the appropriate algorithm needs to be selected according to specific scenarios. In practical applications, we can choose the most suitable algorithm based on factors such as string length, matching degree, memory consumption, etc.

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