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A Binary (Max) Heap is a complete binary tree that maintains the Max Heap property.


Binary Heap is one possible data structure to model an efficient Priority Queue (PQ) Abstract Data Type (ADT). In a PQ, each element has a "priority" and an element with higher priority is served before an element with lower priority (ties are broken with standard First-In First-Out/FIFO rule as with normal Queue). Try clicking ExtractMax() for a sample animation on extracting the max value of random Binary Heap above.


To focus the discussion scope, we design this visualization to show a Binary Max Heap that contains distinct integers only.


Click 'Next' (on the top right)/press 'Page Down' to advance this e-Lecture slide, use the drop down list/press 'Space' to jump to a specific slide, or Click 'X' (on the bottom right)/press 'Esc' to go to Exploration mode.


Remarks: By default, we show e-Lecture Mode for first time (or non logged-in) visitor.
Please login if you are a repeated visitor or register for an (optional) free account first.

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Complete Binary Tree: Every level in the binary tree, except possibly the last/lowest level, is completely filled, and all vertices in the last level are as far left as possible.


Binary Max Heap property: The parent of each vertex - except the root - contains value greater than the value of that vertex. This is an easier-to-verify definition than the following alternative definition: The value of a vertex - except the leaf/leaves - must be greater than the value of its one (or two) child(ren).


Pro-tip: Since you are not logged-in, you may be a first time visitor who are not aware of the following keyboard shortcuts to navigate this e-Lecture mode: [PageDown] to advance to the next slide, [PageUp] to go back to the previous slide, [Esc] to toggle between this e-Lecture mode and exploration mode.

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Priority Queue (PQ) Abstract Data Type (ADT) is similar to normal Queue ADT, but with these two major operations:

  1. Enqueue(x): Put a new element (key) x into the PQ (in some order)
  2. y = Dequeue(): Return an existing element y that has the highest priority (key) in the PQ and if ties, return the one that is inserted first, i.e. back to First-In First-Out (FIFO) behavior of a normal Queue

Another pro-tip: We designed this visualization and this e-Lecture mode to look good on 1366x768 resolution or larger (typical modern laptop resolution in 2017). We recommend using Google Chrome to access VisuAlgo. Go to full screen mode (F11) to enjoy this setup. However, you can use zoom-in (Ctrl +) or zoom-out (Ctrl -) to calibrate this.

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Imagine: You are an Air Traffic Controller (ATC) working in the control tower of an airport.


You have scheduled aircraft X/Y to land in the next 3/6 minutes, respectively. Both have enough fuel for at least the next 15 minutes and both are just 2 minutes away from your airport. You observe that your airport runway is clear at the moment.


In case you do not know, aircraft can be instructed to fly in holding pattern near the airport until the designated landing time.

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You have to attend the live lecture to figure out what happens next...


There will be two options presented to you and you will have to decide:

  1. Raise AND wave your hand if you choose option 1,
  2. Raise your hand but do NOT wave it if you choose option 2,

If none of the two options is reasonable for you, simply do nothing.

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There are several potential usages of PQ ADT in real-life on top of what you have seen just now (only in live lecture).


Discussion: Can you mention a few other real-life situations where a PQ is needed?

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We are able to implement this PQ ADT using (circular) array or Linked List but we will have either slow, i.e. O(N) Enqueue or Dequeue operation.


Discussion: Why?

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Now, let's view the visualisation of a (random) Binary (Max) Heap above. You should see a complete binary tree and all vertices except the root satisfy the Max Heap property (A[parent(i)] > A[i] — remember that we disallow duplicate integers here).


Quiz: Based on this Binary (Max) Heap property, where will the largest integer be located?

Can be anywhere
At the root
At one of the leaf
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Important fact to memorize at this point: If we have a Binary Heap of N elements, since we will store it as a complete binary tree, its height will not be taller than O(log N)


Simple analysis: The size N of a full (more than just a complete) binary tree of height h is always N = 2(h+1)-1, therefore h = log2(N+1)-1 ~= log2 N.


See example above with N = 7 = 2(2+1)-1 or h = log2(7+1)-1 = 2.


This fact is important in the analysis of all Binary Heap-related operations.

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A complete binary tree can be stored efficiently as a compact array A as there is no gap between vertices of a complete binary tree/elements of a compact array. To simplify the navigation operations below, we use 1-based array. VisuAlgo displays the index of each vertex as a red label below each vertex. Read those indices in sorted order from 1 to N, then you will see the vertices of the complete binary tree from top to down, left to right.


This way, we can implement basic binary tree traversal operations with simple index manipulations (with help of bit shift manipulation):

  1. parent(i) = i>>1, index i divided by 2 (integer division),
  2. left(i) = i<<1, index i multiplied by 2,
  3. right(i) = (i<<1)+1, index i multiplied by 2 and added by 1.
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In this visualization, you can perform five (5) standard Binary (Max) Heap operations:

  1. Insert(v) in O(log N)
  2. ExtractMax() in O(log N)
  3. Create(A) - O(N log N) version
  4. Create(A) - O(N) version
  5. HeapSort() - in O(N log N)

There are others possible Binary (Max) Heap operations, but currently we do not elaborate them for pedagogical reason in a certain NUS module.

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Insert(v): Insertion of a new item v into a Binary Max Heap can only be done at the last index N plus 1 to maintain the compact array = complete binary tree property. However, the Max Heap property may still be violated. This operation then fixes Max Heap property from the insertion point upwards (if necessary) and stop when there is no more Max Heap property violation. Now try clicking Insert(v) several times to insert a few random v to the currently displayed Binary (Max Heap).


The fix Max Heap property upwards operation has no standard name, we call it ShiftUp but others may call it BubbleUp or IncreaseKey operation.

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Discussion 1: Do you understand why starting from the insertion point back to the root and swapping a vertex with its parent when there is a Max Heap property violation during insertion is always a correct strategy?


Discussion 2: What is the time complexity of this Insert(v) operation?

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ExtractMax(): The reporting and then the deletion of the maximum element (the root) of a Binary Max Heap requires an existing element to replace the root, otherwise the Binary Max Heap becomes two disjoint subtrees. That element must be the last index N for the same reason: To maintain the compact array = complete binary tree property.


Because we promote a leaf vertex to the root vertex of a Binary Max Heap, it will very likely violates the Max Heap property. ExtractMax() operation then fixes Binary Max Heap property from the root downwards by comparing the current value with the its child/the larger of its two children (if necessary). Now try ExtractMax() on the currently displayed Binary (Max) Heap.


The fix Max Heap property downwards operation has no standard name, we call it ShiftDown but others may call it BubbleDown or Heapify operation.

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Discussion 1: Why if a vertex has two children, we have to check (and possibly swap) that vertex with the larger of its two children during the downwards fix of Max Heap property? Why can't we just compare with the left (or right, if exists) vertex only?


Discussion 2: What is the time complexity of this ExtractMax() operation?

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Up to here, we have a data structure that can implement the two major operations of Priority Queue (PQ) ADT efficiently:

  1. For Enqueue(x), we can use Insert(x) in O(log N) time, and
  2. For y = Dequeue(), we can use y = ExtractMax() in O(log N) time.

However, we can do a few more operations with Binary Heap.

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Create(A): Creates a valid Binary (Max) Heap from an input array A of N integers (comma separated) into an initially empty Binary Max Heap.


There are two variants for this operations, one that is simpler but runs in O(N log N) and a more advanced technique that runs in O(N).


Pro tip: Try opening two copies of VisuAlgo on two browser windows. Execute different Create(A) versions on the worst case 'Sorted example' to see the somewhat dramatic differences of the two.

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Create(A) - O(N log N): Simply insert (that is, by calling Insert(v) operation) all N integers of the input array into an initially empty Binary Max Heap one by one.


Analysis: This operation is clearly O(N log N) as we call O(log N) Insert(v) operation N times. Let's examine the 'Sorted example' which is one of the hard case of this operation (Now try the Hard Case - O(N log N) where we show a case where A=[1,2,3,4,5,6,7] -- please be patient as this example will take some time to complete). If we insert values in increasing order into an initially empty Binary Max Heap, then every insertion triggers a path from the insertion point (a new leaf) upwards to the root.

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Create(A) - O(N): This faster version of Create(A) operation was invented by Robert W. Floyd in 1964. It takes advantage of the fact that a compact array = complete binary tree and all leaves (i.e. half of the vertices — see the next slide) are Binary Max Heap by default. This operation then fixes Binary Max Heap property (if necessary) only from the last internal vertex back to the root.


Analysis: A loose analysis gives another O(N/2 log N) = O(N log N) complexity but it is actually just O(2*N) = O(N) — details in the next few slides. Now try the Hard Case - O(N) on the same input array A=[1,2,3,4,5,6,7] and see that on the same hard case as with the previous slide (but not the one that generates maximum number of swaps), this operation is far superior than the O(N log N) version.

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Simple proof on why half of Binary (Max) Heap of N (without loss of generality, let's assume that N is even) items are leaves are as follows:


Suppose that the last leaf is at index N, then the parent of that last leaf is at index i = N/2. The left child of vertex i+1, if exists (it actually does not exist), will be 2*(i+1) = 2*(N/2+1) = N+2, which exceeds index N (the last leaf) so index i+1 must also be a leaf vertex that has no child. As Binary Heap indexing is consecutive, basically indices [i+1 = N/2+1, i+2 = N/2+2, ..., N], or half of the vertices, are leaves.

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HeapSort(): John William Joseph Williams invented HeapSort() algorithm in 1964, together with this Binary Heap data structure. HeapSort() operation (assuming the Binary Max Heap has been created in O(N)) is very easy. Simply call the O(log N) ExtractMax() operation N times. Now try HeapSort() on the currently displayed Binary (Max) Heap.


Simple Analysis: HeapSort() clearly runs in O(N log N) — an optimal comparison-based sorting algorithm.


Quiz: In worst case scenario, HeapSort() is asymptotically faster than...

Bubble Sort
Merge Sort
Selection Sort
Insertion Sort
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Although HeapSort() runs in θ(N log N) time for all (best/average/worst) cases, is it really the best comparison-based sorting algorithm?


Discussion point: How about caching performance of HeapSort()?

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You have reached the end of the basic stuffs of this Binary (Max) Heap data structure and we encourage you to explore further in the Exploration Mode.


However, we still have a few more interesting Binary (Max) Heap challenges for you that are outlined in this section.


When you have cleared them all, we invite you to study more advanced algorithms that use Priority Queue as (one of) its underlying data structure, like Prim's MST algorithm, Dijkstra's SSSP algorithm, etc.

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If you are looking for an implementation of Binary (Max) Heap to actually model a Priority Queue, then there is a good news.


C++ and Java already have built-in Priority Queue implementations that very likely use this data structure. They are C++ STL priority_queue (the default is a Max Priority Queue) and Java PriorityQueue (the default is a Min Priority Queue).


However, the built-in implementation may not be suitable to do some PQ extended operations efficiently (details omitted) for pedagogical reason in a certain NUS module.


Nevertheless, here is our implementation of BinaryHeapDemo.cpp/java (link TBA).

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For a few more interesting questions about this data structure, please practice on Binary Heap 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 such achievement will be recorded in your user account.

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We also have a few programming problems that somewhat requires the usage of this Binary Heap data structure: UVa 01203 - Argus and Kattis - numbertree.


Try them to consolidate and improve your understanding about this data structure. You are allowed to use C++ STL priority_queue or Java PriorityQueue if that simplifies your implementation.

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

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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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Criar(A) - O(N log N)

Criar(A) - O(N)

Inserir(v)

ExtractMax()

HeapSort()

>
A =

Vai!

Sorted Example

Aleatório

A =

Vai!

Sorted Example

Aleatório

v =

Vai!

Sobre Time Termos de uso

Sobre

O VisuAlgo foi conceitualizado em 2011 por Dr. Steven Halim como uma ferramenta para auxiliar seus estudantes a entenderem melhor estruturas de dados e algoritmos, permitindo que eles aprendessem o básico por conta e em seu próprio ritmo.
VisuAlgo contém muitos algoritmos avançados que são discutidos no livro de Dr. Steven Halim ('Competitive Programming', em co-autoria com seu irmão Dr. Felix Halim) e além. Hoje, algumas visualizações/animações destes algoritmos avançados só podem ser encontrados no VisuAlgo.
Apesar de ter sido especificamente projetado para os estudantes da Universidade Nacional de Singapura (NUS) cursando várias disciplinas de estruturas de dados e algoritmos (ex.: CS1010, CS1020, CS2010, CS2020, CS3230, e CS3230), como defensores do aprendizado online, nós esperamos que mentes curiosas ao redor do mundo achem estas visualizações úteis também.
VisuAlgo não foi projetado para funcionar bem em telas de toque pequenas (ex.: smartphones) desde o  princípio devido à necessidade de atender a muitas visualizações complexas de algoritmos que requerem vários pixels e gestos de clicar-e-arrastar para interação. A resolução mínima para uma experiência de usuário respeitável é 1024x768 e somente a página inicial é relativamente amigável a dispositivos móveis. 
VisuAlgo é um projeto em andamento e mais visualizações complexas ainda estão em desenvolvimento. 
O desenvolvimento mais excitante é o gerador de questões e verificador automático (o sistema de quiz online) que permite aos estudantes testar seus conhecimentos de estruturas de dados e algoritmos básicos. As questões são aleatoriamente geradas através de algumas regras e as respostas dos estudantes são instantaneamente e automaticamente avaliadas assim que são submetidas para o nosso servidor de avaliação. Este sistema de quiz online, quando for adotado por mais instrutores de Ciência da Computação ao redor do mundo, deve tecnicamente eliminar questões manuais sobre estruturas de dados e algoritmos básicos de provas típicas de Ciência da Computação em muitas Universidades. Definindo um peso pequeno (mas não-zero) para aqueles aprovados no quiz online, um instrutor de Ciência da Computação pode (significativamente) melhorar o domínio de seus estudantes sobre estas questões básicas, uma vez que os estudantes têm virtualmente um número infinito de questões para praticar que podem ser verificadas instantaneamente antes que eles possam fazer o quiz online. O modo de treino atualmente contém questões para 12 módulos de visualização. Em breve nós adicionaremos os 8 módulos de visualização restantes, para que todos os módulos de visualização no VisuAlgo tenham um componente de quiz online.
Outro ramo de desenvolvimento em atividade é o subprojeto de internacionalização do VisuAlgo. Nós queremos preparar uma base de dados de termos de Ciência da Computação para todos os textos em inglês que aparecem no sistema VisuAlgo. Esta é uma tarefa grande e requer crowdsourcing. Uma vez que o sistema estiver pronto, nós convidaremos os visitantes do VisuAlgo a contribuir, especialmente se você não for um falante nativo de inglês. Atualmente, nós também temos notas públicas sobre o VisuAlgo em vários idiomas:
zh, id, kr, vn, th.

Time

Líder do Projeto & Conselheiro (Julho de 2011-presente)
Dr Steven Halim, Senior Lecturer, School of Computing (SoC), National University of Singapore (NUS)
Dr Felix Halim, Software Engineer, Google (Mountain View)

Estudantes Pesquisadores de Graduação 1 (Jul 2011-Apr 2012)
Koh Zi Chun, Victor Loh Bo Huai

Projeto Final do Ano/Estudantes do Programa de Oportunidades de Pesquisa para a Graduação (UROP) 1 (Jul 2012-Dec 2013)
Phan Thi Quynh Trang, Peter Phandi, Albert Millardo Tjindradinata, Nguyen Hoang Duy

Projeto Final do Ano/Estudantes do Programa de Oportunidades de Pesquisa para a Graduação (UROP) 2 (Jun 2013-Apr 2014)
Rose Marie Tan Zhao Yun, Ivan Reinaldo

Estudantes Pesquisadores de Graduação 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

Projeto Final do Ano/Estudantes do Programa de Oportunidades de Pesquisa para a Graduação (UROP) 3 (Jun 2014-Apr 2015)
Erin Teo Yi Ling, Wang Zi

Projeto Final do Ano/Estudantes do Programa de Oportunidades de Pesquisa para a Graduação (UROP) 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.

Agradecimentos
Este projeto foi tornado possível pela generosa Concessão de Aperfeiçoamento de Ensino do Centro de Desenvolvimento de Ensino e Aprendizado (CDTL) da Universidade Nacional de Singapura (NUS).

Termos de uso

VisuAlgo é gratuito para a comunidade de Ciência da Computação na Terra. Se você gosta do VisuAlgo, o único pagamento que lhe pedimos é que você fale da existência do VisuAlgo para outros estudantes/instrutores de Ciência da Computação que você conhece =) via Facebook, Twitter, página do curso, blog, email, etc.
Se você é um estudante/instrutor de estruturas de dados e algoritmos, você tem permissão para usar este site diretamente para suas aulas. Se você tirar capturas de tela (vídeos) deste site, você pode usar as capturas de tela (vídeos) em outros lugares desde que você cite a URL deste website (http://visualgo.net) e/ou a lista de publicações abaixo como referência. Contudo, você NÃO tem permissão para baixar os arquivos do VisuAlgo (do lado do cliente) e hospedá-los em seu website, uma vez que isso configura plágio. No momento, nós NÃO permitimos a outras pessoas copiar este projeto e criar variantes do VisuAlgo. Não há problemas em usar a cópia offline (lado do cliente) do VisuAlgo para seu uso pessoal.
Note que o componente do quiz online do VisuAlgo, por natureza, é um componente pesado para os servidores e não há maneira fácil de salvar os scripts e bases de dados do servidor localmente. Atualmente, o público em geral pode apenas usar o 'modo de treinamento' para acessar este sistema de quiz online. Atualmente, o 'modo de prova' é um ambiente mais controlado para usar estas questões geradas randomicamente e verificação automática para um exame real na Universidade Nacional de Singapura (NUS). Outros instrutores de Ciência da Computação interessados devem contatar o prof. Dr. Steven Halim se você quiser experimentar este 'modo de prova'.'

Lista de Publicações

Este trabalho foi apresentado brevemente no CLI Workshop durante a Final Mundial do ACM ICPC 2012 (Polônia, Varsóvia) e na IOI Conference durante a IOI 2012 (Sirmione-Montichiari, Itália). Você pode clicar neste link para ler nosso paper de 2012 sobre este sistema (ele ainda não era chamado VisuAlgo em 2012).
Este trabalho foi feito em sua maioria por meus estudantes anteriores. Os relatórios finais mais recentes estão aqui: Erin, Wang Zi, Rose, Ivan.

Avisos de Bugs ou Solicitações de Novas Funcionalidades

VisuAlgo não é um projeto finalizado. Dr. Steven Halim ainda está ativamente melhorando o VisuAlgo. Se você está usando o VisuAlgo e perceber um bug em qualquer uma de nossas páginas de visualizações/ferramenta de quiz online ou se você quiser solicitar novas funcionalidades, por favor contate o Dr. Steven Halim. O contato dele é a concatenação de seu nome e adicione gmail ponto com.