Is Apple Redistribution into Boxes easy or hard?

Apple Redistribution into Boxes is classified as an Easy problem on LeetCode with a high acceptance rate around 78%. The challenge mainly tests whether you recognize the greedy insight that selecting the largest box capacities first minimizes the number of boxes required.

Apple Redistribution into Boxes Python/Java solution

Most implementations follow the same steps: sum all apples, sort the capacity array in descending order, and accumulate capacities until the total meets or exceeds the apple count. This logic translates directly into Python, Java, C++, JavaScript, and other languages with built-in sorting utilities.

How to solve Apple Redistribution into Boxes in O(n)?

A true O(n) solution generally requires constraints such as bounded capacities that allow counting sort or bucket techniques. In the general case with arbitrary values, the optimal practical solution sorts the capacities and runs in O(m log m) time. The greedy selection of the largest capacities ensures the minimum number of boxes.

What is the best approach for Apple Redistribution into Boxes?

The most efficient approach is a greedy strategy. First calculate the total number of apples, then sort the box capacities in descending order and keep selecting the largest boxes until their combined capacity holds all apples. Sorting costs O(m log m) time where m is the number of boxes, and the rest of the process is linear.

Is Apple Redistribution into Boxes asked at Google/Amazon/Meta?

Problems using greedy selection and capacity allocation patterns frequently appear in interviews at companies like Amazon, Google, and Meta. While this exact problem may vary in wording, the underlying technique of sorting resources and selecting the largest first is a common interview pattern.

What data structure is used in Apple Redistribution into Boxes?

The primary data structure is an array combined with sorting for the greedy solution. An alternative implementation uses a priority queue (max-heap) to repeatedly extract the box with the largest remaining capacity.

What is the time complexity of Apple Redistribution into Boxes?

The optimal solution runs in O(m log m) time due to sorting the box capacities. After sorting, you accumulate capacities until they cover the total number of apples, which takes O(m) time. Space complexity is O(1) if sorting is done in place.

Description Hints 2 Solution Video 9:53

3074. Apple Redistribution into Boxes

Easy78.5% acceptance3 companies

You are given an array apple of size n and an array capacity of size m.

There are n packs where the i^th pack contains apple[i] apples. There are m boxes as well, and the i^th box has a capacity of capacity[i] apples.

Return the minimum number of boxes you need to select to redistribute these n packs of apples into boxes.

Note that, apples from the same pack can be distributed into different boxes.

Example 1:

Input: apple = [1,3,2], capacity = [4,3,1,5,2]
Output: 2
Explanation: We will use boxes with capacities 4 and 5.
It is possible to distribute the apples as the total capacity is greater than or equal to the total number of apples.

Example 2:

Input: apple = [5,5,5], capacity = [2,4,2,7]
Output: 4
Explanation: We will need to use all the boxes.

Constraints:

1 <= n == apple.length <= 50
1 <= m == capacity.length <= 50
1 <= apple[i], capacity[i] <= 50
The input is generated such that it's possible to redistribute packs of apples into boxes.

Asked at 3 companies

Apple

Google Bloomberg

Topics

Hints open one at a time

Hint 1

Sort array capacity in non-decreasing order.

Hint 2

Greedily select boxes with the largest capacities to redistribute apples optimally.

9:53

Still stuck?Read 3 approaches, or watch a walkthrough.

Open solution

LanguageLoading editor…

1class Solution:
2    def minimumBoxes(self, apple: List[int], capacity: List[int]) -> int:
3        

Starter codePython 3Spaces: 4

Examples

Case 1Case 2

Input

[1,3,2]
[4,3,1,5,2]

Input

[5,5,5]
[2,4,2,7]

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