Last Stone Weight - Heap - Easy - LeetCode
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Last Stone Weight - Heap - Easy - LeetCode

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  • 1The Last Stone Weight problem involves smashing the two heaviest stones until at most one stone remains.
  • 2If two stones of equal weight are smashed, both are destroyed; otherwise, the lighter stone is destroyed and the heavier stone's weight is reduced.
  • 3The algorithm uses a max heap to efficiently manage the stones, with a time complexity of O(n log n) and space complexity of O(n).

AI-generated summary · May not capture all nuances

Key Insight
AskGif

"The Last Stone Weight problem involves smashing the two heaviest stones until at most one stone remains."

Last Stone Weight - Heap - Easy - LeetCode

We have a collection of stones, each stone has a positive integer weight.

Each turn, we choose the two heaviest stones and smash them together. Suppose the stones have weights x and y with x <= y. The result of this smash is:

If x == y, both stones are totally destroyed; If x != y, the stone of weight x is totally destroyed, and the stone of weight y has new weight y-x. At the end, there is at most 1 stone left. Return the weight of this stone (or 0 if there are no stones left.)

Example 1:

Input: [2,7,4,1,8,1] Output: 1 Explanation: We combine 7 and 8 to get 1 so the array converts to [2,4,1,1,1] then, we combine 2 and 4 to get 2 so the array converts to [2,1,1,1] then, we combine 2 and 1 to get 1 so the array converts to [1,1,1] then, we combine 1 and 1 to get 0 so the array converts to [1] then that's the value of last stone.

Note:

1 <= stones.length <= 30 1 <= stones[i] <= 1000

public class Solution {
 int[] heap;
 int index = 1;
 public int LastStoneWeight(int[] stones)
 {
 if(stones.Length==0){
 return 0;
 }
 
 if(stones.Length==1){
 return stones[0];
 }
 
 heap = new int[stones.Length + 1];
 for (int i = 0; i < stones.Length; i++)
 {
 Insert(stones[i]);
 }

 int diff = 0;
 while (index-1 > 1)
 {
 int max1 = ExtractMax();
 int max2 = ExtractMax();
 diff = max1 - max2;
 Insert(diff);
 }

 return diff;
 }

 private int ExtractMax()
 {
 int max = heap[1];
 index--;
 heap[1] = heap[index]; 
 HeapifyDown(1);
 return max;
 }

 private void HeapifyDown(int idx)
 {
 int left = 2 * idx;
 int right = 2 * idx + 1;
 if (left > index)
 {
 return;
 }
 if (right > index)
 {
 if (heap[left] > heap[idx])
 {
 int temp = heap[left];
 heap[left] = heap[idx];
 heap[idx] = temp;
 HeapifyDown(left); 
 }
 return;
 }

 if (heap[left] > heap[right] && heap[left] > heap[idx])
 {
 int temp = heap[left];
 heap[left] = heap[idx];
 heap[idx] = temp;
 HeapifyDown(left);
 }
 else if (heap[right] > heap[idx])
 {
 int temp = heap[right];
 heap[right] = heap[idx];
 heap[idx] = temp;
 HeapifyDown(right);
 }
 }

 private void Insert(int val)
 {
 heap[index] = val;
 HeapifyUp(index);
 index++;
 }

 private void HeapifyUp(int idx)
 {
 int parent = idx / 2;
 if (parent < 1)
 {
 return;
 }

 if (heap[parent] < heap[idx])
 {
 int temp = heap[parent];
 heap[parent] = heap[idx];
 heap[idx] = temp;

 HeapifyUp(parent);
 }
 }
}

Time Complexity: O(nlogn)

Space Complexity: O(n)

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sumitc91

Published on 4 October 2020 · 2 min read · 410 words

Part of AskGif Blog · coding

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