1066. Root of AVL Tree (25)

本文介绍AVL树的基本概念及其自我平衡特性,并通过示例演示如何在一系列插入操作后保持AVL树的平衡状态。文章提供了一个C++实现案例,展示了节点旋转等关键操作。

An AVL tree is a self-balancing binary search tree. In an AVL tree, the heights of the two child subtrees of any node differ by at most one; if at any time they differ by more than one, rebalancing is done to restore this property. Figures 1-4 illustrate the rotation rules.

    

    

Now given a sequence of insertions, you are supposed to tell the root of the resulting AVL tree.

Input Specification:

Each input file contains one test case. For each case, the first line contains a positive integer N (<=20) which is the total number of keys to be inserted. Then N distinct integer keys are given in the next line. All the numbers in a line are separated by a space.

Output Specification:

For each test case, print ythe root of the resulting AVL tree in one line.

Sample Input 1:
5
88 70 61 96 120
Sample Output 1:
70
Sample Input 2:
7
88 70 61 96 120 90 65
Sample Output 2:

88

考察AVL树的基本操作,AC代码:

#include<iostream>
using namespace std;

struct treeNode
{
	int val;
	treeNode* left;
	treeNode* right;
};

treeNode* rotateLL(treeNode* root)
{
	treeNode* child = root->left;
	treeNode* temp = child->right;
	child->right = root;
	root->left = temp;
	return child;
}

treeNode* rotateRR(treeNode* root)
{
	treeNode* child = root->right;
	treeNode* temp = child->left;
	child->left = root;
	root->right = temp;
	return child;
}

treeNode* rotateLR(treeNode* root)
{
	root->left = rotateRR(root->left);
	return rotateLL(root);
}

treeNode* rotateRL(treeNode* root)
{
	root->right = rotateLL(root->right);
	return rotateRR(root);
}

int getHeight(treeNode* root)
{
	if(root == NULL)
		return 0;
	else
		return max(getHeight(root->left) + 1, getHeight(root->right) + 1);
}

bool isBalance(treeNode* root)
{
	return abs(getHeight(root->left) - getHeight(root->right)) < 2;
}

treeNode* insert(treeNode* root, int val)
{
	if(root == NULL)
	{
		root =new treeNode();
		root->val = val;
		return root;
	}
	if(val <= root->val)
	{
		root->left = insert(root->left, val);
		if(!isBalance(root))
		{
			if(val <= root->left->val)
			{
				root = rotateLL(root);
			}
			else
			{
				root = rotateLR(root);
			}
		}
	}
	else
	{
		root->right = insert(root->right, val);
		if(!isBalance(root))
		{
			if(val > root->right->val)
			{
				root = rotateRR(root);
			}
			else
			{
				root = rotateRL(root);
			}
		}
	}
	return root;
}

int main()
{
	int n;
	scanf("%d", &n);
	treeNode* root = NULL;
	for(int i = 0; i < n; i++)
	{
		int val;
		scanf("%d", &val);
		root = insert(root, val);
	}
	printf("%d",root->val);
	return 0;
}


内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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