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A Matching in a graph G = (V, E) is a subset M of E edges in G such that no two of which meet at a common vertex.


Maximum Cardinality Matching (MCM) problem is a Graph Matching problem where we seek a matching M that contains the largest possible number of edges. A possible variant is Perfect Matching where all V vertices are matched, i.e. the cardinality of M is V/2.


A Bipartite Graph is a graph whose vertices can be partitioned into two disjoint sets X and Y such that every edge can only connect a vertex in X to a vertex in Y.


Maximum Cardinality Bipartite Matching (MCBM) problem is the MCM problem in a Bipartite Graph, which is a lot easier than MCM problem in a General Graph.


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This visualization is currently limited to unweighted graphs only. Thus, we currently do not support Graph Matching problem variants involving weighted graphs...


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To switch between the unweighted MCBM (default, as it is much more popular) and unweighted MCM mode, click the respective header.


Here is an example of MCM mode. In MCM mode, one can draw a General, not necessarily Bipartite graphs. However, the graphs are unweighted (all edges have uniform weight 1).


The available algorithms are different in the two modes.


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You can view the visualisation here!


For Bipartite Graph visualization, we will re-layout the vertices of the graph so that the two disjoint sets are clearly visible as Left and Right sets. For General Graph, we do not relayout the vertices.


Initially, edges have grey color. Matched edges will have black color. Free/Matched edges along an augmenting path will have Orange/Light Blue colors, respectively.

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There are three different sources for specifying an input graph:

  1. Draw Graph: You can draw any undirected unweighted graph as the input graph (note that in MCBM mode, the drawn input graph will be relayout into a nice Bipartite graph layout during algorithm animation),
  2. Modeling: A lot of graph problems can be reduced into an MCBM problem. In this visualization, we have the modeling examples for the famous Rook Attack problem (currently disabled) and standard MCBM problem (also valid in MCM mode).
  3. Examples: You can select from the list of our example graphs to get you started. The list of examples are slightly different in the two MCBM vs MCM modes.
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There are several Max Cardinality Bipartite Matching (MCBM) algorithms in this visualization, plus one more in Max Flow visualization:

  1. O(VE) Augmenting Path Algorithm (without greedy pre-processing),
  2. O(√(V)E) Dinic's Max Flow Algorithm, see Max Flow visualization, select Modeling → Bipartite Matching → All 1, then use Dinic's algorithm.
  3. O(√(V)E) Hopcroft Karp's Algorithm,
  4. O(kE) Augmenting Path Algorithm++ (with randomized greedy pre-processing),

PS: Although possible, we will likely not use O(V3) Edmonds's Matching Algorithm if the input is guaranteed to be a Bipartite Graph.

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Augmenting Path is a path that starts from a free (unmatched) vertex u in graph G, alternates through unmatched, match, ..., unmatched edges in G, until it ends at another free vertex v. If we flip the edge status along that augmenting path, we will increase the number of edges in the matching set M by 1 and eliminates this augmenting path.


In 1957, Claude Berge proposes the following lemma: A matching M in graph G is maximum iff there is no more augmenting path in G.


The Augmenting Path Algorithm is a simple O(V*(V+E)) = O(V2 + VE) = O(VE) implementation of that lemma (on Bipartite Graph): Find and then eliminate augmenting paths in Bipartite Graph G. Click Augmenting Path Algorithm Demo to visualize this algorithm on the currently displayed random Bipartite Graph.

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vi match, vis; // global variables

int Aug(int L) { // return 1 if ∃ an augmenting path from L
if (vis[L]) return 0; // return 0 otherwise
vis[L] = 1;
for (auto &v : AL[L]) {
int R = v.first;
if (match[R] == -1 || Aug(match[R])) {
match[R] = L;
return 1; // found 1 matching
} }
return 0; // no matching
}
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// in int main(), build the bipartite graph
// use directed edges from left set (of size VLeft) to right set
int MCBM = 0;
match.assign(V, -1);
for (int L = 0; L < VLeft; L++) {
vis.assign(VLeft, 0);
MCBM += Aug(L); // find augmenting path starting from L
}
printf("Found %d matchings\n", MCBM);

You can also download ch4_09_mcbm.cpp/java from Competitive Programming 3 companion website.

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If we are given a Complete Bipartite Graph KN/2,N/2, i.e.
V = N/2+N/2 = N and E = N/2×N/2 = N2/4 ≈ N2, then
the Augmenting Path Algorithm discussed earlier will run in O(VE) = O(N×N2) = O(N3).


This is only OK for V ∈ [400..500].


Try executing the standard Augmenting Path Algorithm on this Extreme Test Case, which is an almost complete K5,5 Bipartite Graph.

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The MCBM problem can be modeled as a Max Flow problem. Go to Max Flow visualization page and see the flow graph modeling of MCBM problem (select Modeling → Bipartite Matching → all 1).


If we use one of the fastest Max Flow algorithm, i.e. Dinic's algorithm on this flow graph, we can find Max Flow = MCBM in O(√(V)E) time — analysis omitted for now. This allows us to solve MCBM problem with V ∈ [1 000..1 500].

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The key idea of Hopcroft Karp's (HK) Algorithm (invented in 1973) is identical to Dinic's Max Flow Algorithm discussed earlier, i.e. prioritize shortest augmenting paths (in terms of number of edges used) first. That's it, augmenting paths with 1 edge are processed first before longer augmenting paths with 3 edges, 5 edges, 7 edges, etc (the length always increase by 2 due to the nature of augmenting path in a Bipartite Graph).


Hopcroft Karp's Algorithm has time complexity of O(√(V)E) — analysis omitted for now. This allows us to solve MCBM problem with V ∈ [1 000..1 500].


Try HK Algorithm on the same Extreme Test Case earlier. You will notice that HK Algorithm can find the MCBM in a much faster time than the previous standard O(VE) Augmenting Path Algorithm.

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However, we can actually make the easy-to-code Augmenting Path Algorithm discussed earlier to avoid its worst case O(VE) behavior by doing O(V+E) randomized (to avoid adversary test case) greedy pre-processing before running the actual algorithm.


This O(V+E) additional pre-processing step is simple: For every vertex on the left set, match it with a randomly chosen unmatched neighbouring vertex on the right set. This way, we eliminates many trivial (one-edge) Augmenting Paths that consist of a free vertex u, an unmatched edge (u, v), and a free vertex v.


Try Augmenting Path Algorithm++ on the same Extreme Test Case earlier. Notice that the pre-processing step already eliminates many trivial 1-edge augmenting paths, making the actual Augmenting Path Algorithm only need to do little amount of additional work.

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Quite often, on randomly generated Bipartite Graph, the randomized greedy pre-processing step has cleared most of the matchings.


However, we can construct test case like: Examples: Randomized Greedy Processing Killer to make randomization as ineffective as possible. For every group of 4 vertices, there are 2 matchings. Random greedy processing has 50% chance of making mistake per group. Try this Hard Test Case case to see for yourself.


The worst case time complexity is no longer O(VE) but now O(kE) where k is a small integer, much smaller than V, k can be as small as 0 and is at most V/2. In our experiments, we estimate k to be "about √(V)" too. This version of Augmenting Path Algorithm++ allows us to solve MCBM problem with V ∈ [1 000..1 500] too.

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There are two Max Cardinality Matching (MCM) algorithms in this visualization:

  1. O(V^3) Edmonds's Matching algorithm (without greedy pre-processing),
  2. O(V^3) Edmonds's Matching algorithm (with greedy pre-processing),
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In General Graph, we may have Odd-Length cycle. Augmenting Path is not well defined in such graph, hence we cannot directly implement Claude Berge's lemma like what we did with Bipartite Graph.


Jack Edmonds call a path that starts from a free vertex u, alternates between free, matched, ..., free edges, and returns to the same free vertex u as Blossom. This situation is only possible if we have Odd-Length cycle, i.e. non-Bipartite Graph. Edmonds then proposed Blossom shrinking/contraction and expansion algorithm to solve this issue, details verbally.


This algorithm can be implemented in O(V^3).

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As with the Augmenting Path Algorithm++ for the MCBM problem, we can also do randomized greedy pre-processing step to eliminate as many 'trivial matchings' as possible upfront. This reduces the amount of work of Edmonds's Matching Algorithm, thus resulting in a faster time complexity — analysis TBA.

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We have not added visualizations for weighted variant of MCBM and MCM problems (future work).

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As the action is being carried out, each step will be described in the status panel.

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e-Lecture: The content of this slide is hidden and only available for selected CS lecturer who knows Dr Steven Halim personally.

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Control the animation with the player controls! Keyboard shortcuts are:

Spacebar: play/pause/replay
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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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Draw Graph

Modeling

Examples

Augmenting Path

>

Rook Attack

GO

Generate Random Bipartite Graph

Undirected Max Flow Killer

House of Cards

CS4234 Tutorial 3

F-mod

Randomized Greedy Processing Killer

K5,5

K5,5 (Almost)

Standard

With Randomized Greedy Preprocessing

Hopcroft Karp

Edmonds Blossom

Edmonds Blossom + Greedy

About Team Terms of use

About

VisuAlgo was conceptualised in 2011 by Dr Steven Halim as a tool to help his students better understand data structures and algorithms, by allowing them to learn the basics on their own and at their own pace.

VisuAlgo contains many advanced algorithms that are discussed in Dr Steven Halim's book ('Competitive Programming', co-authored with his brother Dr Felix Halim) and beyond. Today, some of these advanced algorithms visualization/animation can only be found in VisuAlgo.

Though specifically designed for National University of Singapore (NUS) students taking various data structure and algorithm classes (e.g. CS1010, CS1020, CS2010, CS2020, CS3230, and CS3230), as advocators of online learning, we hope that curious minds around the world will find these visualisations useful too.

VisuAlgo is not designed to work well on small touch screens (e.g. smartphones) from the outset due to the need to cater for many complex algorithm visualizations that require lots of pixels and click-and-drag gestures for interaction. The minimum screen resolution for a respectable user experience is 1024x768 and only the landing page is relatively mobile-friendly.

VisuAlgo is an ongoing project and more complex visualisations are still being developed.

The most exciting development is the automated question generator and verifier (the online quiz system) that allows students to test their knowledge of basic data structures and algorithms. The questions are randomly generated via some rules and students' answers are instantly and automatically graded upon submission to our grading server. This online quiz system, when it is adopted by more CS instructors worldwide, should technically eliminate manual basic data structure and algorithm questions from typical Computer Science examinations in many Universities. By setting a small (but non-zero) weightage on passing the online quiz, a CS instructor can (significantly) increase his/her students mastery on these basic questions as the students have virtually infinite number of training questions that can be verified instantly before they take the online quiz. The training mode currently contains questions for 12 visualization modules. We will soon add the remaining 8 visualization modules so that every visualization module in VisuAlgo have online quiz component.

Another active branch of development is the internationalization sub-project of VisuAlgo. We want to prepare a database of CS terminologies for all English text that ever appear in VisuAlgo system. This is a big task and requires crowdsourcing. Once the system is ready, we will invite VisuAlgo visitors to contribute, especially if you are not a native English speaker. Currently, we have also written public notes about VisuAlgo in various languages: zh, id, kr, vn, th.

Team

Project Leader & Advisor (Jul 2011-present)
Dr Steven Halim, Senior Lecturer, School of Computing (SoC), National University of Singapore (NUS)
Dr Felix Halim, Software Engineer, Google (Mountain View)

Undergraduate Student Researchers 1 (Jul 2011-Apr 2012)
Koh Zi Chun, Victor Loh Bo Huai

Final Year Project/UROP students 1 (Jul 2012-Dec 2013)
Phan Thi Quynh Trang, Peter Phandi, Albert Millardo Tjindradinata, Nguyen Hoang Duy

Final Year Project/UROP students 2 (Jun 2013-Apr 2014)
Rose Marie Tan Zhao Yun, Ivan Reinaldo

Undergraduate Student Researchers 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

Final Year Project/UROP students 3 (Jun 2014-Apr 2015)
Erin Teo Yi Ling, Wang Zi

Final Year Project/UROP students 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.

Acknowledgements
This project is made possible by the generous Teaching Enhancement Grant from NUS Centre for Development of Teaching and Learning (CDTL).

Terms of use

VisuAlgo is free of charge for Computer Science community on earth. If you like VisuAlgo, the only payment that we ask of you is for you to tell the existence of VisuAlgo to other Computer Science students/instructors that you know =) via Facebook, Twitter, course webpage, blog review, email, etc.

If you are a data structure and algorithm student/instructor, you are allowed to use this website directly for your classes. If you take screen shots (videos) from this website, you can use the screen shots (videos) elsewhere as long as you cite the URL of this website (http://visualgo.net) and/or list of publications below as reference. However, you are NOT allowed to download VisuAlgo (client-side) files and host it on your own website as it is plagiarism. As of now, we do NOT allow other people to fork this project and create variants of VisuAlgo. Using the offline copy of (client-side) VisuAlgo for your personal usage is fine.

Note that VisuAlgo's online quiz component is by nature has heavy server-side component and there is no easy way to save the server-side scripts and databases locally. Currently, the general public can only use the 'training mode' to access these online quiz system. Currently the 'test mode' is a more controlled environment for using these randomly generated questions and automatic verification for a real examination in NUS. Other interested CS instructor should contact Steven if you want to try such 'test mode'.

List of Publications

This work has been presented briefly at the CLI Workshop at the ACM ICPC World Finals 2012 (Poland, Warsaw) and at the IOI Conference at IOI 2012 (Sirmione-Montichiari, Italy). You can click this link to read our 2012 paper about this system (it was not yet called VisuAlgo back in 2012).

This work is done mostly by my past students. The most recent final reports are here: Erin, Wang Zi, Rose, Ivan.

Bug Reports or Request for New Features

VisuAlgo is not a finished project. Dr Steven Halim is still actively improving VisuAlgo. If you are using VisuAlgo and spot a bug in any of our visualization page/online quiz tool or if you want to request for new features, please contact Dr Steven Halim. His contact is the concatenation of his name and add gmail dot com.