How do you solve Apple Redistribution into Boxes on LeetCode?

Apple Redistribution into Boxes (LeetCode #3074) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Greedy algorithms often yield optimal solutions for resource allocation problems.

What is the time complexity of Apple Redistribution into Boxes?

The optimal solution for Apple Redistribution into Boxes runs in O(n log n) time and uses O(1) space. The time complexity is O(n log n) due to the sorting step, while the space complexity is O(1) as we are using constant space for variables.

What is the brute force solution for Apple Redistribution into Boxes?

The brute force approach for Apple Redistribution into Boxes is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach tries every possible combination of boxes to find the minimum number needed to hold all apples. This is simple but inefficient as it checks all combinations. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Apple Redistribution into Boxes?

Apple Redistribution into Boxes uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting, Array.

What are the interview tips for Apple Redistribution into Boxes?

Always clarify the problem constraints and requirements before diving into coding. Think about edge cases, such as when all boxes are needed or when only one box can suffice. Practice explaining your thought process as you code, as communication is key in interviews.

What are common mistakes when solving Apple Redistribution into Boxes?

Overlooking the need to sort the capacities before selection. Failing to check if the total capacity meets the requirement early enough.

#3074

Apple Redistribution into Boxes

Easy

ArrayGreedySortingGreedySortingArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n log n)Space O(1)

The optimal approach sorts the box capacities in descending order and uses a greedy strategy to fill the boxes starting from the largest. This ensures we use the least number of boxes needed to accommodate all apples.

⚙️

Algorithm

3 steps

1Step 1: Calculate the total number of apples from all packs.
2Step 2: Sort the box capacities in descending order.
3Step 3: Iterate through the sorted capacities, adding boxes until the total capacity meets or exceeds the total apples.

solution.py11 lines

1def min_boxes_optimal(apple, capacity):
2    total_apples = sum(apple)
3    capacity.sort(reverse=True)
4    current_capacity = 0
5    box_count = 0
6    for cap in capacity:
7        current_capacity += cap
8        box_count += 1
9        if current_capacity >= total_apples:
10            return box_count
11    return box_count

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting step, while the space complexity is O(1) as we are using constant space for variables.

1Greedy algorithms often yield optimal solutions for resource allocation problems.
2Sorting can simplify the problem by allowing us to make better choices first.

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