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A Spanning Tree (ST) of a connected undirected weighted graph G is a subgraph of G that is a tree and connects (spans) all vertices of G. A graph G can have multiple STs, each with different total weight (the sum of edge weights in the ST).


A Min(imum) Spanning Tree (MST) of G is an ST of G that has the smallest total weight among the various STs.


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The MST problem is a standard graph (and also optimization) problem defined as follows: Given a connected undirected weighted graph G = (V, E), select a subset of edges of G such that the graph is still connected but with minimum total weight. The output is either the actual MST of G (there can be several possible MSTs of G) or usually just the minimum total weight itself (unique).


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Imagine that you work for a government who wants to link all rural villages in the country with roads.
(that is spanning tree).


The cost to build a road to connect two villages depends on the terrain, distance, etc.
(that is a complete undirected weighted graph).


You want to minimize the total building cost. How are you going to build the roads?
(that is minimum spanning tree).


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The MST problem has polynomial solutions.


In this visualization, we will learn two of them: Kruskal's algorithm and Prim's algorithm. Both are classified as Greedy Algorithms.

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View the visualisation of MST algorithm on the left.


Originally, all vertices and edges in the input graph are colored with the standard black color on white background.


At the end of the MST algorithm, MST edges (and all vertices) will be colored orange and Non-MST edges will be colored grey.

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There are two different sources for specifying an input graph:

  1. Draw Graph: You can draw any connected undirected weighted graph as the input graph.
  2. Example Graphs: You can select from the list of example connected undirected weighted graphs to get you started.
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Kruskal's algorithm: An O(E log V) greedy MST algorithm that grows a forest of minimum spanning trees and eventually combine them into one MST.


Kruskal's requires a good sorting algorithm to sort edges of the input graph by increasing weight and another data structure called Union-Find Disjoint Sets (UFDS) to help in checking/preventing cycle.

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Kruskal's algorithm first sort the set of edges E in non-decreasing weight (there can be edges with the same weight), and if ties, by increasing smaller vertex number of the edge, and if still ties, by increasing larger vertex number of the edge.


Discussion: Is this the only possible sort criteria?

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Then, Kruskal's algorithm will perform a loop through these sorted edges (that already have non-decreasing weight property) and greedily taking the next edge e if it does not create any cycle w.r.t edges that have been taken earlier.


Without further ado, let's try Kruskal on the default example graph (that has three edges with the same weight). Go through this animated example first before continuing.

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To see on why the Greedy Strategy of Kruskal's algorithm works, we define a loop invariant: Every edge e that is added into tree T by Kruskal's algorithm is part of the MST.


At the start of Kruskal's main loop, T = {} is always part of MST by definition.


Kruskal's has a special cycle check in its main loop (using UFDS data structure) and only add an edge e into T if it will never form a cycle w.r.t the previously selected edges.


At the end of the main loop, Kruskal's can only select V-1 edges from a connected undirected weighted graph G without having any cycle. This implies that Kruskal's produces a Spanning Tree.


On the default example, notice that after taking the first 2 edges: 0-1 and 0-3, in that order, Kruskal's cannot take edge 1-3 as it will cause a cycle 0-1-3-0. Kruskal's then take edge 0-2 but it cannot take edge 2-3 as it will cause cycle 0-2-3-0.

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We have seen in the previous slide that Kruskal's algorithm will produce a tree T that is a Spanning Tree (ST) when it stops. But is it the minimum ST, i.e. the MST?


To prove this, we need to recall that before running Kruskal's main loop, we have already sort the edges in non-decreasing weight, i.e. the latter edges will have equal or larger weight than the earlier edges.

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At the start of every loop, T is always part of MST.


If Kruskal's only add a legal edge e (that will not cause cycle w.r.t the edges that have been taken earlier) with min cost, then we can be sure that w(T U e) ≤ w(T U any other unprocessed edge e' that does not form cycle) (by virtue that Kruskal's has sorted the edges, so w(e) ≤ w(e').


Therefore, at the end of the loop, the Spanning Tree T must have minimal overall weight w(T), so T is the final MST.


On the default example, notice that after taking the first 2 edges: 0-1 and 0-3, in that order, and ignoring edge 1-3 as it will cause a cycle 0-1-3-0. We can safely take the next smallest legal edge 0-2 (with weight 2) as taking any other legal edge (e.g. edge 2-3 with larger weight 3) will either create another MST with equal weight (not in this example) or another ST that is not minimum (which is this example).

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There are two parts of Kruskal's algorithm: Sorting and the Kruskal's main loop.


The sorting of edges is easy. We just store the graph using Edge List data structure and sort E edges using any O(E log E) = O(E log V) sorting algorithm (or just use C++/Java sorting library routine) by increasing weight, smaller vertex number, higher vertex number. This O(E log V) is the bottleneck part of Kruskal's algorithm as the second part is actually lighter, see below.


Kruskal's main loop can be easily implemented using Union-Find Disjoint Sets data structure. We use IsSameSet(u, v) to test if taking edge e with endpoints u and v will cause a cycle (same connected component) or not. If IsSameSet(u, v) returns false, we greedily take this next smallest and legal edge e and call UnionSet(u, v) to prevent future cycles involving this edge. This part runs in O(E) as we assume UFDS IsSameSet(u, v) and UnionSet(u, v) operations run in O(1) for a relatively small graph.

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Prim's algorithm: Another O(E log V) greedy MST algorithm that grows a Minimum Spanning Tree from a starting source vertex until it spans the entire graph.


Prim's requires a Priority Queue data structure (usually implemented using Binary Heap) to dynamically order the currently considered edges based on increasing weight, an Adjacency List data structure for fast neighbor enumeration of a vertex, and a Boolean array to help in checking cycle.


Another name of Prim's algorithm is Jarnik-Prim's algorithm.

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Prim's algorithm starts from a designated source vertex s and enqueues all edges incident to s into a Priority Queue (PQ) according to increasing weight, and if ties, by increasing vertex number (of the neighboring vertex number). Then it will repeatedly do the following greedy steps: If the vertex v of the front-most edge pair information e: (w, v) in the PQ has not been visited, it means that we can greedily extends the tree T to include vertex v and enqueue edges connected to v into the PQ, otherwise we discard edge e.


Without further ado, let's try Prim(1) on the default example graph (that has three edges with the same weight). That's it, we start Prim's algorithm from source vertex s = 1. Go through this animated example first before continuing.

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Prim's algorithm is a Greedy Algorithm because at each step of its main loop, it always try to select the next valid edge e with minimal weight (that is greedy!).


The convince us that Prim's algorithm is correct, let's go through the following simple proof: Let T be the spanning tree of graph G generated by Prim's algorithm and T* be the spanning tree of G that is known to have minimal cost, i.e. T* is the MST.


If T == T*, that's it, Prim's algorithm produces exactly the same MST as T*, we are done.


But if T != T*...

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Assume that on the default example, T = {0-1, 0-3, 0-2} but T* = {0-1, 1-3, 0-2} instead.


Let ek = (u, v) be the first edge chosen by Prim's Algorithm at the k-th iteration that is not in T* (on the default example, k = 2, e2 = (0, 3), note that (0, 3) is not in T*).


Let P be the path from u to v in T*, and let e* be an edge in P such that one endpoint is in the tree generated at the (k−1)-th iteration of Prim's algorithm and the other is not (on the default example, P = 0-1-3 and e* = (1, 3), note that vertex 1 is inside T at first iteration k = 1).

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If the weight of e* is less than the weight of ek, then Prim's algorithm would have chosen e* on its k-th iteration as that is how Prim's algorithm works.


So, it is certain that w(e*) ≥ w(ek).
(on the example graph, e* = (1, 3) has weight 1 and ek = (0, 3) also has weight 1).


When weight e* is = weight ek, the choice between the e* or ek is actually arbitrary. And whether the weight of e* is ≥ weight of ek, e* can always be substituted with ek while preserving minimal total weight of T*. (on the example graph, when we replace e* = (1, 3) with ek = (0, 3), we manage to transform T* into T).

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But if T != T*... (continued)


We can repeat the substitution process outlined earlier repeatedly until T* = T and thereby we have shown that the spanning tree generated by any instance of Prim's algorithm (from any source vertex s) is an MST as whatever the optimal MST is, it can be transformed to the output of Prim's algorithm.

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We can easily implement Prim's algorithm with two well-known data structures:

  1. A Priority Queue PQ (Binary Heap or just use C++ STL priority_queue/Java PriorityQueue), and
  2. A Boolean array of size V (to decide if a vertex has been taken or not, i.e. in the same connected component as source vertex s or not).

With these, we can run Prim's Algorithm in O(E log V) because we process each edge once and each time, we call Insert((w, v)) and (w, v) = ExtractMax() from a PQ in O(log E) = O(log V2) = O(2 log V) = O(log V). As there are E edges, Prim's Algorithm runs in O(E log V).

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Quiz: Having seen both Kruskal's and Prim's Algorithms, which one is the better MST algorithm?

Prim's Algorithm
It Depends
Kruskal's Algorithm


Discussion: Why?

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You have reached the end of the basic stuffs of this Min(imum) Spanning Tree graph problem and its two classic algorithms: Kruskal's and Prim's (there are others, like Boruvka's, but not discussed in this visualization). We encourage you to explore further in the Exploration Mode.


However, the harder MST problems can be (much) more challenging that its basic version.


Once you have (roughly) mastered this MST topic, we encourage you to study more on harder graph problems where MST is used as a component, e.g. approximation algorithm for NP-hard (Metric No-Repeat) TSP and Steiner Tree (soon) problems.

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For a few more challenging questions about this MST problem and/or Kruskal's/Prim's Algorithms, please practice on MST training module (no login is required, but short and of medium difficulty setting only).


However, for registered users, you should login and then go to the Main Training Page to officially clear this module (and its pre-requisites) and such achievement will be recorded in your user account.


Pro-tip: To attempt MST Online Quiz in easy or medium difficulty setting without having to clear the pre-requisites first, you have to log out first (from your profile page).

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This MST problem can be much more challenging than this basic form. Therefore we encourage you to try the following two ACM ICPC contest problems about MST: UVa 01234 - RACING and Kattis - arcticnetwork.


Try them to consolidate and improve your understanding about this graph problem. You are allowed to use/modify our implementation code for Kruskal's/Prim's Algorithm that can be downloaded Here (ch4_03_kruskal_prim.cpp/java, from the companion Competitive Programming book website).

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当操作进行时,状态面板将会有每个步骤的描述。
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Control the animation with the player controls! Keyboard shortcuts are:

Spacebar: play/pause/replay
Left/right arrows: step backward/step forward
-/+: decrease/increase speed
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Return to 'Exploration Mode' to start exploring!


Note that if you notice any bug in this visualization or if you want to request for a new visualization feature, do not hesitate to drop an email to the project leader: Dr Steven Halim via his email address: stevenhalim at gmail dot com.

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绘制图表

图示

Kruskal's Algorithm

Prim's Algorithm(s)

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CP 4.10

CP 4.14

K5

Rail

Tessellation

s =

执行

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关于

VisuAlgo在2011年由Steven Halim博士概念化,作为一个工具,帮助他的学生更好地理解数据结构和算法,让他们自己和自己的步伐学习基础。
VisuAlgo包含许多高级算法,这些算法在Steven Halim博士的书(“竞争规划”,与他的兄弟Felix Halim博士合作)和其他书中讨论。今天,一些高级算法的可视化/动画只能在VisuAlgo中找到。
虽然专门为新加坡国立大学(NUS)学生采取各种数据结构和算法类(例如CS1010,CS1020,CS2010,CS2020,CS3230和CS3230),作为在线学习的倡导者,我们希望世界各地的好奇心发现这些可视化也很有用。
VisuAlgo不是从一开始就设计为在小触摸屏(例如智能手机)上工作良好,因为需要满足许多复杂的算法可视化,需要大量的像素和点击并拖动手势进行交互。一个令人尊敬的用户体验的最低屏幕分辨率为1024x768,并且只有着陆页相对适合移动设备。
VisuAlgo是一个正在进行的项目,更复杂的可视化仍在开发中。
最令人兴奋的发展是自动问题生成器和验证器(在线测验系统),允许学生测试他们的基本数据结构和算法的知识。这些问题是通过一些规则随机生成的,学生的答案会在提交给我们的评分服务器后立即自动分级。这个在线测验系统,当它被更多的世界各地的CS教师采用,应该技术上消除许多大学的典型计算机科学考试手动基本数据结构和算法问题。通过在通过在线测验时设置小(但非零)的重量,CS教练可以(显着地)增加他/她的学生掌握这些基本问题,因为学生具有几乎无限数量的可以立即被验证的训练问题他们参加在线测验。培训模式目前包含12个可视化模块的问题。我们将很快添加剩余的8个可视化模块,以便VisuAlgo中的每个可视化模块都有在线测验组件。
另一个积极的发展分支是VisuAlgo的国际化子项目。我们要为VisuAlgo系统中出现的所有英语文本准备一个CS术语的数据库。这是一个很大的任务,需要众包。一旦系统准备就绪,我们将邀请VisuAlgo游客贡献,特别是如果你不是英语母语者。目前,我们还以各种语言写了有关VisuAlgo的公共注释:
zh, id, kr, vn, th.

团队

项目领导和顾问(2011年7月至今)
Dr Steven Halim, Senior Lecturer, School of Computing (SoC), National University of Singapore (NUS)
Dr Felix Halim, Software Engineer, Google (Mountain View)

本科生研究人员 1 (Jul 2011-Apr 2012)
Koh Zi Chun, Victor Loh Bo Huai

最后一年项目/ UROP学生 1 (Jul 2012-Dec 2013)
Phan Thi Quynh Trang, Peter Phandi, Albert Millardo Tjindradinata, Nguyen Hoang Duy

最后一年项目/ UROP学生 2 (Jun 2013-Apr 2014)
Rose Marie Tan Zhao Yun, Ivan Reinaldo

本科生研究人员 2 (May 2014-Jul 2014)
Jonathan Irvin Gunawan, Nathan Azaria, Ian Leow Tze Wei, Nguyen Viet Dung, Nguyen Khac Tung, Steven Kester Yuwono, Cao Shengze, Mohan Jishnu

最后一年项目/ UROP学生 3 (Jun 2014-Apr 2015)
Erin Teo Yi Ling, Wang Zi

最后一年项目/ UROP学生 4 (Jun 2016-Dec 2017)
Truong Ngoc Khanh, John Kevin Tjahjadi, Gabriella Michelle, Muhammad Rais Fathin Mudzakir

List of translators who have contributed ≥100 translations can be found at statistics page.

致谢
这个项目是由来自NUS教学与学习发展中心(CDTL)的慷慨的教学增进赠款提供的。

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请注意,VisuAlgo的在线测验组件本质上具有沉重的服务器端组件,并且没有简单的方法来在本地保存服务器端脚本和数据库。目前,公众只能使用“培训模式”来访问这些在线测验系统。目前,“测试模式”是一个更受控制的环境,用于使用这些随机生成的问题和自动验证在NUS的实际检查。其他感兴趣的CS教练应该联系史蒂文如果你想尝试这样的“测试模式”。
出版物名单
这项工作在2012年ACM ICPC世界总决赛(波兰,华沙)和IOI 2012年IOI大会(意大利Sirmione-Montichiari)的CLI讲习班上进行了简要介绍。您可以点击此链接阅读我们2012年关于这个系统的文章(它在2012年还没有被称为VisuAlgo)。
这项工作主要由我过去的学生完成。最近的最后报告是:Erin,Wang Zi,Rose,Ivan。
错误申报或请求添加新功能
VisuAlgo不是一个完成的项目。 Steven Halim博士仍在积极改进VisuAlgo。如果您在使用VisuAlgo并在我们的可视化页面/在线测验工具中发现错误,或者如果您想要求添加新功能,请联系Dr Steven Halim博士。他的联系邮箱是他的名字加谷歌邮箱后缀:StevenHalim@gmail.com。