How do you solve Super Washing Machines on LeetCode?

Super Washing Machines (LeetCode #517) 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: The total number of dresses must be divisible by the number of machines for a solution to exist.

What is the brute force solution for Super Washing Machines?

The brute force approach for Super Washing Machines is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves simulating every possible move of dresses between adjacent washing machines until all machines have the same number of dresses. This is straightforward but inefficient, as it can lead to many unnecessary computations. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Super Washing Machines?

Super Washing Machines uses the following concepts: Array, Greedy. The recommended patterns to study are: Greedy, Array.

What are the interview tips for Super Washing Machines?

Always start by checking the feasibility of the problem before diving into the solution. Think about edge cases, such as all machines already having the same number of dresses. Practice explaining your thought process as you solve the problem to improve your clarity and communication.

What are common mistakes when solving Super Washing Machines?

Failing to check if the total number of dresses is divisible by the number of machines. Not considering the balance of dresses correctly, leading to incorrect move calculations.

#517

Super Washing Machines

Hard

ArrayGreedyGreedyArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n)Space O(1)

The optimal solution leverages the idea of balancing the dresses based on the target number and the excess or deficit of dresses in each machine. By calculating the maximum moves required based on the excess and deficit, we can efficiently determine the minimum moves needed.

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Algorithm

4 steps

1Step 1: Calculate the total number of dresses and check if it can be evenly distributed among all machines.
2Step 2: Determine the target number of dresses for each machine.
3Step 3: Iterate through each machine to calculate the maximum moves required based on the excess and deficit of dresses.
4Step 4: Return the maximum of the calculated moves.

solution.py14 lines

1# Full working Python code
2
3def findMinMoves(machines):
4    total = sum(machines)
5    n = len(machines)
6    if total % n != 0:
7        return -1
8    target = total // n
9    max_moves = 0
10    current_balance = 0
11    for m in machines:
12        current_balance += m - target
13        max_moves = max(max_moves, abs(current_balance), m - target)
14    return max_moves

ℹ

Complexity note: The time complexity is O(n) because we only need to iterate through the array once to calculate the necessary moves, making it linear in relation to the number of washing machines.

1The total number of dresses must be divisible by the number of machines for a solution to exist.
2Balancing the dresses involves understanding both the excess and deficit of dresses in each machine.

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