Union-Find Algrithm is used to check whether two components are connected or not.

Examples:

By using the graph, we can easily find whether two components are connected or not, if there is no such graph, how do we know whether two components are connected or not?

Answer: For all connected components, we set their "root" to be the same.

So, we use an array to record the root of each component, if their roots are the same, return true, otherwise, return false.

Example:

So, how to implement it?

How is the time complexity of the operations?

So, Union operation is kind of expensive, can we decrease it?

Yes, instead of making all the components use the same root id, we can just set the parent id of root component in one set to the root id of another set. (Why we cannot just set the parent id of the root component in one set to the id of the connected component in another set???)

Example:

But the problem is when we check whether two components have the same root, the worst case time complexity is O(n). n refers to the size of the components, and this happens when we have a thin tree (all components are in the same tree, but this tree has no branches.)

Time complexity:

So, the approach above cannot decrease the union operation time complexity, rather, it increases the find operation time complexity.

If we have a closer look, we can find the reason why quick-union approach is not performing well is because the height of the tree could be very tall. So, the question becomes how to decrease the height of th tree?

There are two approaches:

First, when we marge two trees, the root of the smaller tree (with less # of components) will be connected to the root of larger tree.

The benefit of doing this can decrease the height of the tree.

Another approach is called path compression. The idea is every time when we get the root of a component, we always set its parent id to the root id.

Example:

So, this approach can also decrease the height of the tree.

Reference:https://www.cs.duke.edu/courses/cps100e/fall09/notes/UnionFind.pdf (普林斯顿的这位老爷爷讲得真的很清楚,youtube上可以收到他的视频。)

Union-Find Algorithm的更多相关文章

  1. [慢查优化]建索引时注意字段选择性 & 范围查询注意组合索引的字段顺序

    文章转自:http://www.cnblogs.com/zhengyun_ustc/p/slowquery2.html 写在前面的话: 之前曾说过"不要求每个人一定理解 联表查询(join/ ...

  2. [MySQL Reference Manual] 8 优化

    8.优化 8.优化 8.1 优化概述 8.2 优化SQL语句 8.2.1 优化SELECT语句 8.2.1.1 SELECT语句的速度 8.2.1.2 WHERE子句优化 8.2.1.3 Range优 ...

  3. 8.2.1.4 Index Merge Optimization 索引合并优化:

    8.2.1.4 Index Merge Optimization 索引合并优化: 索引合并方法是用于检索记录 使用多个 范围扫描和合并它们的结果集到一起 mysql> show index fr ...

  4. MySQL Index Merge Optimization

    Index Merge用在通过一些range scans得到检索数据行和合并成一个整体.合并可以通过 unions,intersections,或者unions-intersection运用在底层的扫 ...

  5. [Swift]LeetCode990. 等式方程的可满足性 | Satisfiability of Equality Equations

    Given an array equations of strings that represent relationships between variables, each string equa ...

  6. mysql 调优 来自5.6版本官方手册

    注意:下面示例中的key1和key2代表两个索引,key_part1和key_part2代表一个复合索引的第一列和第二列.non_key代表非索引列. 优化SQL语句 where语句优化: mysql ...

  7. Mysql优化(出自官方文档) - 第一篇(SQL优化系列)

    Mysql优化(出自官方文档) - 第一篇 目录 Mysql优化(出自官方文档) - 第一篇 1 WHERE Clause Optimization 2 Range Optimization Skip ...

  8. Algorithm partI 第2节课 Union−Find

    发展一个有效算法的具体(一般)过程: union-find用来解决dynamic connectivity,下面主要讲quick find和quick union及其应用和改进. 基本操作:find/ ...

  9. Geeks Union-Find Algorithm Union By Rank and Path Compression 图环算法

    相同是查找一个图是否有环的算法,可是这个算法非常牛逼,构造树的时候能够达到O(lgn)时间效率.n代表顶点数 原因是依据须要缩减了树的高度,也叫压缩路径(Path compression),名字非常高 ...

  10. Leetcode: Number of Islands II && Summary of Union Find

    A 2d grid map of m rows and n columns is initially filled with water. We may perform an addLand oper ...

随机推荐

  1. thinkphp 3.2响应头 x-powered-by 修改

    起初是看到千图网的登录链接 查看到的 自己做的网站也看了下 修改的办法就是TP3.2.2 的框架里 具体路径是D:\www\ThinkPHP\Library\Think\View.class.php ...

  2. ssh-keygen不是内部或外部命令

    在**/Git/usr/bin目录下找到ssh-keygen.exe,将**/Git/usr/bin路径添加到环境变量中

  3. iOS数据库学习(1)-安装Navicat

    1.下载Navicat Premium 11.0.16.dmg 已经放到百度网盘,里面有安装文件和注册机 下载链接: http://pan.baidu.com/s/1sjI64HZ  密码: 2h7q ...

  4. php/js获取客户端mac地址的实现代码

    这篇文章主要介绍了如何在php与js中分别获取客户度mac地址的方法,需要的朋友可以参考下   废话不多讲,直接上代码吧! 复制代码 代码如下: <?php   class MacAddr {  ...

  5. sql sever 字符串函数

    SQL Server之字符串函数   以下所有例子均Studnet表为例:  计算字符串长度len()用来计算字符串的长度 select sname ,len(sname) from student ...

  6. MySQL性能优化的最佳经验,随时补充

    1.为查询优化你的查询 大多数的MySQL服务器都开启了查询缓存.这是提高性最有效的方法之一,而且这是被MySQL的数据库引擎处理的.当有很多相同的查询被执行了多次的时候,这些查询结果会被放到一个缓存 ...

  7. 不挣扎了,开始学习LINQ TO XML,进而来解析网页。

    找到了别人遇到和我一样的问题:http://ylad.codeplex.com/discussions/430095(英文) 一位叫做Mister Goodcat的提供了信息: Short answe ...

  8. qstring与char*、基本数据类型的转换

    1.qstring转化为char* QString.toStdString.c_str() 2.char*转化为QString str = QString(QLatin1String(mm)); 3. ...

  9. springMVC之国际化

    1.工程结构 2.jar包 3.配置文件spring-config.xml,springMVC配置文件 <?xml version="1.0" encoding=" ...

  10. flexbox-CSS3弹性盒模型flexbox完整版教程

    原文链接:http://caibaojian.com/flexbox-guide.html flexbox-CSS3弹性盒模型flexbox完整版教程 A-A+ 前端博客•2014-05-08•前端开 ...