2719. Count of Integers
Description
You are given two numeric strings num1
and num2
and two integers max_sum
and min_sum
. We denote an integer x
to be good if:
num1 <= x <= num2
min_sum <= digit_sum(x) <= max_sum
.
Return the number of good integers. Since the answer may be large, return it modulo 109 + 7
.
Note that digit_sum(x)
denotes the sum of the digits of x
.
Example 1:
Input: num1 = "1", num2 = "12", min_sum = 1, max_sum = 8 Output: 11 Explanation: There are 11 integers whose sum of digits lies between 1 and 8 are 1,2,3,4,5,6,7,8,10,11, and 12. Thus, we return 11.
Example 2:
Input: num1 = "1", num2 = "5", min_sum = 1, max_sum = 5 Output: 5 Explanation: The 5 integers whose sum of digits lies between 1 and 5 are 1,2,3,4, and 5. Thus, we return 5.
Constraints:
1 <= num1 <= num2 <= 1022
1 <= min_sum <= max_sum <= 400
Solutions
Solution 1: Digit DP
The problem is actually asking for the number of integers in the range $[num1,..num2]$ whose digit sum is in the range $[min_sum,..max_sum]$. For this kind of range $[l,..r]$ problem, we can consider transforming it into finding the answers for $[1,..r]$ and $[1,..l-1]$, and then subtracting the latter from the former.
For the answer to $[1,..r]$, we can use digit DP to solve it. We design a function $dfs(pos, s, limit)$, which represents the number of schemes when we are currently processing the $pos$th digit, the digit sum is $s$, and whether the current number has an upper limit $limit$. Here, $pos$ is enumerated from high to low.
For $dfs(pos, s, limit)$, we can enumerate the value of the current digit $i$, and then recursively calculate $dfs(pos+1, s+i, limit \bigcap i==up)$, where $up$ represents the upper limit of the current digit. If $limit$ is true, then $up$ is the upper limit of the current digit, otherwise $up$ is $9$. If $pos$ is greater than or equal to the length of $num$, then we can judge whether $s$ is in the range $[min_sum,..max_sum]$. If it is, return $1$, otherwise return $0$.
The time complexity is $O(10 \times n \times max_sum)$, and the space complexity is $O(n \times max_sum)$. Here, $n$ represents the length of $num$.
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