[DP]357. Count Numbers with Unique Digits

本文介绍了一个算法问题,即计算在给定范围内具有唯一数字的整数的数量。通过使用动态规划和排列组合的方法,文章详细解释了解决方案的设计思路,并提供了一段C++代码实现。

题目:

Given a non-negative integer n, count all numbers with unique digits, x, where 0 ≤ x < 10n.

Example:
Given n = 2, return 91. (The answer should be the total numbers in the range of 0 ≤ x < 100, excluding [11,22,33,44,55,66,77,88,99])


题目分析:

1、比较容易看出,题目要求我们根据给出的非负数 n 求出在 0 ≤ x < 10n 范围中所有满足每一位数都不相同的整数x个数;

2、首先分析对于让每一位数都不相同的计算方法,这是一个排列组合问题。入手的角度有两种:直接算出每一位的都不重复的数的个数、先算出有重复的数的个数,再用总的整数数量来作差。对于后者,我们需要对重复的位数和位置进行讨论才能求出的答案,过程很复杂且难于转化为代码;对于前者,如果整数的位数确定,那么我们只需要每一次添加进一个数让他与每个数都不相同即可,如果之前已经有N个数(N < n且N < 10),那么再添加进一个数不重复的概率就是N/10,当然如果N >= 10了,那么已经把0 - 9的所有数字已经占用,则无论添加进什么数都会导致重复。经过这样一轮比较,很明显应该用第一种算法计算固定位数的无重复数字的个数。

3、有了2中的算法,还需要将 0 ≤ x < 10n 中的所有数分割为几个不同的数字长度区间[0 - 9]、[10 - 99]……,这里就很容易发现这些区间在n取不同值的时候的划分规则是相同的,具体而言这个过程是覆盖了了更小n的划分区间,那么这里就可以利用动态规划来建立n = n0和n = n0 - 1的关系,利用n - 1的结果来求的n的答案。

4、在具体实现中有几个需要注意的地方:

      1)题目要求的n为非负数,所以要考虑到n = 0和n = 1的情况,作为动态规划终止条件n = 0时返回1,即 0 ≤ x<1 中有一个数满足条件,即0这个数;

      2)当n > 10时,由于已经不可能存在更多的新增符合条件的数,那么直接取n = 10的结果作为答案即可,无需做无用的递归;

      3)在每个区间运算过程中有很多参数都可以约去,最后的表达式比较简单,但是需要细致的化简。


代码:

int p(int n){
    int mul = 1;
    for(int i = 9; i >= 10 - n; i --)
        mul *= i;
    return mul;
    
}

class Solution {
public:
    int countNumbersWithUniqueDigits(int n) {
        if(n == 0)
            return 1;
        else if(n <= 10)
            return countNumbersWithUniqueDigits(n - 1) + 9 * p(n - 1);
        else
            return countNumbersWithUniqueDigits(10);
    }
};


翻译并用 latex 渲染: First, let&#39;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&#39;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&#39;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&#39;s take a closer look at what such a string might look like: si∈{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 .
08-26
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