2457. Minimum Addition to Make Integer Beautiful
Description
You are given two positive integers n
and target
.
An integer is considered beautiful if the sum of its digits is less than or equal to target
.
Return the minimum non-negative integer x
such that n + x
is beautiful. The input will be generated such that it is always possible to make n
beautiful.
Example 1:
Input: n = 16, target = 6 Output: 4 Explanation: Initially n is 16 and its digit sum is 1 + 6 = 7. After adding 4, n becomes 20 and digit sum becomes 2 + 0 = 2. It can be shown that we can not make n beautiful with adding non-negative integer less than 4.
Example 2:
Input: n = 467, target = 6 Output: 33 Explanation: Initially n is 467 and its digit sum is 4 + 6 + 7 = 17. After adding 33, n becomes 500 and digit sum becomes 5 + 0 + 0 = 5. It can be shown that we can not make n beautiful with adding non-negative integer less than 33.
Example 3:
Input: n = 1, target = 1 Output: 0 Explanation: Initially n is 1 and its digit sum is 1, which is already smaller than or equal to target.
Constraints:
1 <= n <= 1012
1 <= target <= 150
- The input will be generated such that it is always possible to make
n
beautiful.
Solutions
Solution 1: Greedy Algorithm
We define a function \(f(x)\) to represent the sum of the digits of an integer \(x\). The problem is to find the minimum non-negative integer \(x\) such that \(f(n + x) \leq target\).
If the sum of the digits of \(y = n+x\) is greater than \(target\), we can loop through the following operations to reduce the sum of the digits of \(y\) to less than or equal to \(target\):
- Find the lowest non-zero digit of \(y\), reduce it to \(0\), and add \(1\) to the digit one place higher;
- Update \(x\) and continue the above operation until the sum of the digits of \(n+x\) is less than or equal to \(target\).
After the loop ends, return \(x\).
For example, if \(n=467\) and \(target=6\), the change process of \(n\) is as follows:
The time complexity is \(O(\log^2 n)\), where \(n\) is the integer given in the problem. The space complexity is \(O(1)\).
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