How do you solve Maximum Bags With Full Capacity of Rocks on LeetCode?

Maximum Bags With Full Capacity of Rocks (LeetCode #2279) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(n) space complexity. Key insight: Filling bags that need the least additional rocks first maximizes the number of bags filled.

What is the time complexity of Maximum Bags With Full Capacity of Rocks?

The optimal solution for Maximum Bags With Full Capacity of Rocks runs in O(n log n) time and uses O(n) space. The complexity is O(n log n) due to the sorting step, which is the most time-consuming operation in this approach.

What is the brute force solution for Maximum Bags With Full Capacity of Rocks?

The brute force approach for Maximum Bags With Full Capacity of Rocks is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves checking every possible combination of bags to see how many can be filled to capacity using the additional rocks. This is straightforward but inefficient as it doesn't leverage any optimization. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximum Bags With Full Capacity of Rocks?

Maximum Bags With Full Capacity of Rocks uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting, Array.

What are the interview tips for Maximum Bags With Full Capacity of Rocks?

Always consider a greedy approach for optimization problems. Clarify constraints and edge cases before diving into coding. Explain your thought process clearly while coding to demonstrate your understanding.

What are common mistakes when solving Maximum Bags With Full Capacity of Rocks?

Not sorting the bags based on the additional rocks needed. Failing to account for the total number of additional rocks available.

#2279

Maximum Bags With Full Capacity of Rocks

Medium

ArrayGreedySortingGreedySortingArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

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

The optimal approach sorts the bags based on how many additional rocks they need to reach full capacity. By filling the bags that need the least additional rocks first, we maximize the number of bags filled.

⚙️

Algorithm

3 steps

1Step 1: Create an array of the additional rocks needed for each bag to reach full capacity.
2Step 2: Sort this array in ascending order.
3Step 3: Iterate through the sorted array, using the additional rocks to fill the bags until you run out of additional rocks.

solution.py14 lines

1# Full working Python code
2
3def maxBagsOptimal(capacity, rocks, additionalRocks):
4    needed = [capacity[i] - rocks[i] for i in range(len(capacity))]
5    needed.sort()
6    count = 0
7    for n in needed:
8        if additionalRocks >= n:
9            additionalRocks -= n
10            count += 1
11        else:
12            break
13    return count
14

ℹ

Complexity note: The complexity is O(n log n) due to the sorting step, which is the most time-consuming operation in this approach.

1Filling bags that need the least additional rocks first maximizes the number of bags filled.
2Sorting helps in efficiently determining which bags to fill.

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