Leetcode – Last Stone Weight Solution

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This article delves into the “Last Stone Weight” problem, laying out its statement, inherent challenges, possible solution strategies, and efficient Python implementations.

Table of Contents:

  1. Problem Statement
  2. Understanding the Problem Dynamics
  3. Solution Approaches
  4. Python Implementations
  5. Testing and Benchmarking
  6. Concluding Insights

1. Problem Statement:

We have a collection of stones, each stone has a positive integer weight. Every turn, we choose the two heaviest stones and smash them together. If the stones have different weights, the smaller stone is destroyed, and the bigger stone’s weight is reduced by the smaller stone’s weight. If they have the same weight, both stones are completely destroyed. We continue this process until there is either one stone left or none. The task is to return the weight of the last stone (or 0 if no stones remain).

Example:

Input: [2,7,4,1,8,1]
Output: 1

2. Understanding the Problem Dynamics:

The dynamics of this problem center around always selecting the two largest stones. This property hints at the use of a priority mechanism or sorting. Furthermore, after every operation, the resultant stone (if any) has to be placed back in the collection, making dynamic reordering a necessity.

3. Solution Approaches:

  1. Sorting Approach: Continually sort the array and pick the two largest stones. This approach, while intuitive, is less efficient due to the constant need for sorting.
  2. Priority Queue (Max-Heap) Approach: Utilize a priority queue (or max-heap in Python using the heapq library) to always retrieve the heaviest stones efficiently.

4. Python Implementations:

1. Sorting Approach:

def lastStoneWeight(stones):
    while len(stones) > 1:
        stones.sort()  # Sort the stones
        y = stones.pop()  # Get the heaviest stone
        x = stones.pop()  # Get the second heaviest stone
        
        if x != y:  # If they're not of the same weight
            stones.append(y - x)  # Add the resultant stone back to the collection
            
    return stones[0] if stones else 0

2. Priority Queue (Max-Heap) Approach:

import heapq

def lastStoneWeightHeap(stones):
    # Convert the stones into a max-heap (negate the weights for this)
    stones = [-stone for stone in stones]
    heapq.heapify(stones)
    
    while len(stones) > 1:
        y = -heapq.heappop(stones)  # Get the heaviest stone (negate to get the original weight)
        x = -heapq.heappop(stones)  # Get the second heaviest stone
        
        if x != y:  # If they're not of the same weight
            heapq.heappush(stones, -(y - x))  # Add the resultant stone back to the heap
            
    return -stones[0] if stones else 0

5. Testing and Benchmarking:

After constructing the solution, it’s imperative to test on various test cases:

print(lastStoneWeight([2,7,4,1,8,1]))  # Expected output: 1
print(lastStoneWeightHeap([2,7,4,1,8,1]))  # Expected output: 1

print(lastStoneWeight([1,3]))  # Expected output: 2
print(lastStoneWeightHeap([1,3]))  # Expected output: 2

For larger test cases, the max-heap solution should show noticeably better performance due to its logarithmic time complexity in handling stones, compared to the linearithmic time complexity of the sorting approach.

6. Concluding Insights:

The “Last Stone Weight” problem beautifully underscores the importance of understanding the underlying dynamics of a problem. It demonstrates that with the right insights, seemingly complex challenges can be simplified using the appropriate data structures or algorithms. In this case, the max-heap provided a significant boost in efficiency over the more intuitive sorting method.

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