[DP]392. Is Subsequence

本文介绍了一个简单的算法,用于判断一个字符串是否为另一个字符串的子序列。通过递归方法实现了高效检查,时间复杂度为O(len(s)+len(t))。

题目:

Given a string s and a string t, check if s is subsequence of t.

You may assume that there is only lower case English letters in both s and t. t is potentially a very long (length ~= 500,000) string, and s is a short string (<=100).

A subsequence of a string is a new string which is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (ie, "ace" is a subsequence of "abcde" while "aec" is not).

Example 1:
s
= "abc", t = "ahbgdc"

Return true.

Example 2:
s
= "axc", t = "ahbgdc"

Return false.

题目分析:

1、题目要求根据传入的两个字符串s和t,判断s是否为t的子序列;

2、该题算法的构思比较简单。按从头到尾的顺序,先取出s的第一个字符,从t的头部开始依次向尾检验直至找到匹配项,此后再从s中取出第二个字符从t中找到匹配项的后一项开始继续往后检验,直至s取完或者t取完。

3、代码设计方面,设置两个参数int sL和int tL表示当前取出元素的下标,当找到匹配项后,sL和tL都++。设置新函数f引入tL和sL两个新参数,利用递归的方法来实现代码的简化。由于每个字符只检验一次,那么函数的时间复杂度为O(len(s)+len(n))。

4、递归的终止条件分为两个:sL == s.size(),即表示sL已经到s的末尾,s已检验完毕,返回true;tL = t.size(),即tL已到t末尾,t已检验完,返回false。

代码:

bool f(string &s, int sL, string &t, int tL){
    if(sL == s.size())
        return true;
        
    while(s[sL] != t[tL] && tL < t.size())
        tL++;
        
    if(tL == t.size())
        return false;
        
    sL++;
    tL++;
    
    return f(s, sL, t, tL);
}


class Solution {
public:
    bool isSubsequence(string s, string t){
        return f(s, 0, t, 0);
    }
};

代码完成中有几个细节需要处理:
1、特殊情况,比如s为空的情况。因此考虑将sL == s.size()放到函数开头,因为当sL = s.size() = 0时依然为true,且s[sL]会发生数组越界,必须放在其前。
2、将tL == t.size()放在循环的后面,因为若寻找不到匹配项循环跳出后实际上 tL = t.size(),应此时检验并返回false。
3、f引入两个字符串参数时应进行取址调用,否则将造成大量的空间申明。发生memory limit exceeded的错误。
翻译并用 latex 渲染: First, let's see how many zebra-Like numbers less than or equal to 1018 exist. It turns out there are only 30 of them, and based on some zebra-like number zi , the next one can be calculated using the formula zi+1=4⋅zi+1 . Then, we have to be able to quickly calculate the zebra value for an arbitrary number x . Since each subsequent zebra-like number is approximately 4 times larger than the previous one, intuitively, it seems like a greedy algorithm should be optimal: for any number x , we can determine its zebra value by subtracting the largest zebra-like number that does not exceed x , until x becomes 0 . Let's prove the correctness of the greedy algorithm: Assume that y is the smallest number for which the greedy algorithm does not work, meaning that in the optimal decomposition of y into zebra-like numbers, the largest zebra-like number zi that does not exceed y does not appear at all. If the greedy algorithm works for all numbers less than y , then in the decomposition of the number y , there must be at least one number zi−1 . And since y−zi−1 can be decomposed greedily and will contain at least 3 numbers zi−1 , we will end up with at least 4 numbers zi−1 in the decomposition. Moreover, there will be at least 5 numbers in the decomposition because 4⋅zi−1<zi , which means it is also less than y . Therefore, if the fifth number is 1 , we simply combine 4⋅zi−1 with 1 to obtain zi ; otherwise, we decompose the fifth number into 4 smaller numbers plus 1 , and we also combine this 1 with 4⋅zi−1 to get zi . Thus, the new decomposition of the number y into zebra-like numbers will have no more numbers than the old one, but it will include the number zi — the maximum zebra-like number that does not exceed y . This means that y can be decomposed greedily. We have reached a contradiction; therefore, the greedy algorithm works for any positive number. Now, let's express the greedy decomposition of the number x in a more convenient form. We will represent the decomposition as a string s of length 30 consisting of digits, where the i -th character will denote how many zebra numbers zi are present in this decomposition. Let's take a closer look at what such a string might look like: si&isin;{0,1,2,3,4} ; if si=4 , then for any j<i , the character sj=0 (this follows from the proof of the greedy algorithm). Moreover, any number generates a unique string of this form. This is very similar to representing a number in a new numeric system, which we will call zebroid. In summary, the problem has been reduced to counting the number of numbers in the interval [l,r] such that the sum of the digits in the zebroid numeral system equals x . This is a standard problem that can be solved using dynamic programming on digits. Instead of counting the suitable numbers in the interval [l,r] , we will count the suitable numbers in the intervals [1,l] and [1,r] and subtract the first from the second to get the answer. Let dp[ind][sum][num_less_m][was_4] be the number of numbers in the interval [1,m] such that: they have ind+1 digits; the sum of the digits equals sum ; num_less_m=0 if the prefix of ind+1 digits of the number m is lexicographically greater than these numbers, otherwise num_less_m=1 ; was_4=0 if there has not been a 4 in the ind+1 digits of these numbers yet, otherwise was_4=1 . Transitions in this dynamic programming are not very difficult — they are basically appending a new digit at the end. The complexity of the solution is O(log2A) , if we estimate the number of zebra-like numbers up to A=1018 as logA .
最新发布
08-26
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