Given preorder and inorder traversal of a tree, construct the binary tree.
Note:
You may assume that duplicates do not exist in the tree.
这种类型的题目经常考选择题。对于这道题,采用的是分治的思想,通过preorder的第一个元素为根元素,将inorder分成两部分,也将preorder分成两部分。这样每个部分又是相同的子问题。解决完这两个子问题,就是得到了当前结点的左右子树,将其合并即可。
public class Solution {
private TreeNode partitionalMerge(int[] preorder, int pS, int pE, int[] inorder ,int iS, int iE){
if(pS >pE || iS > iE) return null;
if(pS == pE) return new TreeNode(preorder[pS]);
int mid = 0;
for(mid =iS; mid<=iE; mid++){
if(inorder[mid] == preorder[pS])break;
}
TreeNode left = partitionalMerge(preorder, pS+1, pS+mid-iS, inorder, iS, mid-1);
TreeNode right = partitionalMerge(preorder, pS+mid-iS+1, pE, inorder, mid+1, iE);
TreeNode root = new TreeNode(preorder[pS]);
root.left = left;
root.right = right;
return root;
}
public TreeNode buildTree(int[] preorder, int[] inorder) {
if(preorder.length != inorder.length) return null;//exception
if(preorder == null) return null;
return partitionalMerge(preorder, 0, preorder.length-1, inorder, 0, inorder.length-1);
}
}
难度是索引坐标经常算错。这道题的最坏时间复杂度为O(NLogN),空间复杂度为O(LogN+N),其中N为数组长度, LogN为递归占用空间,N为树所用的节点空间。
时间复杂度中的N为搜索的最坏时间,那么如果我们将搜索算法修改让其时间复杂度为O(1),这样整体的时间复杂度为O(N)。
/**
* Definition for a binary tree node.
* public class TreeNode {
* int val;
* TreeNode left;
* TreeNode right;
* TreeNode(int x) { val = x; }
* }
*/
public class Solution {
private TreeNode partitionalMerge(int[] preorder, int pS, int pE ,int iS, int iE, HashMap<Integer, Integer> map){
if(pS >pE || iS > iE) return null;
if(pS == pE) return new TreeNode(preorder[pS]);
int mid = 0;
mid =map.get(preorder[pS]);
TreeNode left = partitionalMerge(preorder, pS+1, pS+mid-iS, iS, mid-1, map);
TreeNode right = partitionalMerge(preorder, pS+mid-iS+1, pE, mid+1, iE, map);
TreeNode root = new TreeNode(preorder[pS]);
root.left = left;
root.right = right;
return root;
}
public TreeNode buildTree(int[] preorder, int[] inorder) {
if(preorder.length != inorder.length) return null;//exception
if(preorder == null) return null;
HashMap<Integer, Integer> map = new HashMap<>();
for(int i = 0; i<inorder.length; i++){
map.put(inorder[i], i);
}
return partitionalMerge(preorder, 0, preorder.length-1, 0, inorder.length-1, map);
}
}
计算时间从20多ms降低到了6ms。