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 \(num\).