How do you solve Minimum Element After Replacement With Digit Sum on LeetCode?

Minimum Element After Replacement With Digit Sum (LeetCode #3300) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Directly summing digits using arithmetic is more efficient than string manipulation.

What is the brute force solution for Minimum Element After Replacement With Digit Sum?

The brute force approach for Minimum Element After Replacement With Digit Sum is Brute Force, with O(n²) time complexity and O(1) space complexity. We can convert each number to a string, split it into its digits, and sum them up. This approach is straightforward but inefficient because we repeat the digit-summing process for each number. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Minimum Element After Replacement With Digit Sum?

Minimum Element After Replacement With Digit Sum uses the following concepts: Array, Math. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Minimum Element After Replacement With Digit Sum?

Always consider edge cases, such as the smallest and largest numbers in the input. Explain your thought process clearly while coding, as it shows your understanding. Practice writing clean and efficient code, as readability is important in interviews.

What are common mistakes when solving Minimum Element After Replacement With Digit Sum?

Forgetting to handle single-digit numbers correctly. Using string manipulation unnecessarily, which can slow down the solution.

#3300

Minimum Element After Replacement With Digit Sum

Easy

ArrayMathHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

Instead of converting each number to a string, we can directly compute the digit sum using arithmetic operations. This is more efficient and avoids the overhead of string manipulation.

⚙️

Algorithm

4 steps

1Step 1: Initialize a variable to hold the minimum value.
2Step 2: For each number in the array, compute the digit sum using a loop.
3Step 3: Compare the computed sum with the current minimum and update if necessary.
4Step 4: Return the minimum value after processing all numbers.

solution.py17 lines

1# Full working Python code
2
3def digit_sum(n):
4    sum = 0
5    while n > 0:
6        sum += n % 10
7        n //= 10
8    return sum
9
10def min_element_after_replacement(nums):
11    min_value = float('inf')
12    for num in nums:
13        min_value = min(min_value, digit_sum(num))
14    return min_value
15
16# Example usage
17print(min_element_after_replacement([10, 12, 13, 14]))  # Output: 1

ℹ

Complexity note: The time complexity is O(n) because we only loop through the array once, and for each number, we compute the digit sum in O(log m) time. However, since log m is a constant factor for our input constraints, we can consider it O(n).

1Directly summing digits using arithmetic is more efficient than string manipulation.
2The minimum digit sum can be found in a single pass through the array.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.