You are given an array apple of size n and an array capacity of size m.
There are n packs where the ith pack contains apple[i] apples. There are m boxes as well, and the ith 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 <= 501 <= m == capacity.length <= 501 <= apple[i], capacity[i] <= 50This approach suggests sorting the box capacities in descending order and continuously filling the boxes until all apple packs are distributed. The greedy nature ensures we utilize boxes with maximum capacity first, minimizing the total number of boxes used.
This C function uses a greedy algorithm. First, it calculates the total number of apples, then sorts the capacity array in descending order. It iteratively subtracts the largest available box capacity from the total apple count, until all apples are redistributed. The number of iterations gives the minimum number of boxes used.
C++
Java
Python
C#
JavaScript
Time Complexity: O(m log m) due to sorting, and O(m) for iterating over the boxes. Total is O(m log m).
Space Complexity: O(1), as the sorting is done in place.
This approach is alternative to the greedy method, which leverages a priority queue (min-heap) to manage capacities and ensures that the smallest required boxes are filled first. This can be useful in scenarios where random access to the smallest element is necessary. Although it may not be optimal for the current arrangement, it provides a perspective on alternative sorting.
In C++, the standard library provides a priority queue, which can be used for managing smallest elements efficiently. The code leverages this to consistently use the smallest box capacity still needed and adjusts the total until all apples are distributed.
Python
Time Complexity: O(m log m) for heap management operations.
Space Complexity: O(m) since space is required for the priority queue.
| Approach | Complexity |
|---|---|
| Greedy Approach for Minimizing Box Usage | Time Complexity: O(m log m) due to sorting, and O(m) for iterating over the boxes. Total is O(m log m). |
| Use of Priority Queue (Min-Heap Structure) | Time Complexity: O(m log m) for heap management operations. |
Apple Coding Interview Question - Apple Redistribution into Boxes • Greg Hogg • 111,726 views views
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