Complete Binary Trees

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Del curso dictado por University of California San Diego

Data Structures

1782 calificaciones

University of California San Diego

Data Structures

1782 calificaciones

Curso 2 de 6 en SpecializationData Structures and Algorithms

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!

De la lección

Priority Queues and Disjoint Sets

We start this module by considering priority queues which are used to efficiently schedule jobs, either in the context of a computer operating system or in real life, to sort huge files, which is the most important building block for any Big Data processing algorithm, and to efficiently compute shortest paths in graphs, which is a topic we will cover in our next course. For this reason, priority queues have built-in implementations in many programming languages, including C++, Java, and Python. We will see that these implementations are based on a beautiful idea of storing a complete binary tree in an array that allows to implement all priority queue methods in just few lines of code. We will then switch to disjoint sets data structure that is used, for example, in dynamic graph connectivity and image processing. We will see again how simple and natural ideas lead to an implementation that is both easy to code and very efficient. By completing this module, you will be able to implement both these data structures efficiently from scratch.

Binary Trees1:25

Basic Operations12:41

Complete Binary Trees9:27

Conoce a los instructores

Alexander S. Kulikov
Visiting Professor
Department of Computer Science and Engineering
Michael Levin
Lecturer
Computer Science
Daniel M Kane
Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics
Neil Rhodes
Adjunct Faculty
Computer Science and Engineering

Our goal in this video is to design a way of keeping

the height of our binary max heap shallow.

Well, what is a natural approach to create a tree out of n given nodes,

whose height is as small as possible.

Well, it is natural to require that all the levels are fully packed, right.

This leads us to a notion of a complete binary tree.

By definition a binary tree is called complete if all its levels are filled

completely.

Except possibly the last one where we require additionally that all

the nodes at this last level are in left most positions.

Let me illustrate this with a few small examples.

So this is a complete binary tree.

This is also a complete binary tree, and this is also a complete binary tree.

So this is a binary complete tree too.

And this is our first example of a binary tree which is not complete.

Well it is not complete because on the last level

the two nodes shown here are not in the left most positions.

This is also not a complete binary tree.

This binary tree is also not complete

because well this child is missing here, right.

And this is also an example of a binary tree which is not complete.

The first advantage of complete binary trees is straightforward, and

it is exactly what we need actually.

Namely, the height of any complete binary tree with n nodes is O(log n).

Intuitively, this is clear.

A complete binary tree with n nodes has the minimum possible

height over all binary trees with n nodes.

Well, just because all the levels of this tree, except possibly the last one,

are fully packed.

Still let me give you a formal proof.

Well, for this consider our complete binary tree and

let me show a small example.

So I assume that this is our complete binary tree.

So in this case, n = 10 and the number of levels, l = 4.

Well, let's first do the following thing, let's complete the last level.

And let's denote the result in number of nodes by n prime.

In this case in particular the number of nodes

in the new tree is equal to 15.

Well the first thing to note is that n prime is at most 2n.

Well this is just because in such a tree where all levels including

the last one are fully packed, the number of nodes on each level

is equal to the number of nodes on all the previous levels minus one.

Okay, so for example here the number of nodes on the last level is 8,

and the number of nodes on all previous levels is 7.

So we added at most seven vertices.

Now, when we have such a tree where all the levels are packed completely,

it is easy to relate the number of levels with the number of vertices.

Namely with the number of nodes.

Namely n prime = to 2 to the l- 1.

This allows us to conclude that l = binary logarithm of n prime + 1.

Now, recall that n prime is at most 2n, which allows us to write that l

is at most binary logarithm of 2n + 1 which is of course, O(log n).

The second advantage of complete binary trees is not so

straightforward, but fortunately it is still easy to describe.

To explain it, let's consider again a toy example,

I mean a complete binary tree shown here on this slide.

Let's enumerate all its nodes going from top to down, and

on each level from left to right.

So this way the root receives number 1,

to its children receive numbers 2 and 3 and so on.

So it turns out that such a numbering allows for each vertex,

number i for example, to be specific, to compute the number of its parent and

the numbers of each children using the following simple formulas.

Once again, if we have a node number i,

then its parent has number i divided by 2 and

rounded down while its two children have numbers 2i and 2i + 1.

To give a specific example, I assume that i = 4,

which means that we are speaking about about this node.

Then to find out the number of its parent,

we need to divide i by 2, this gives us 2.

And indeed, vertex number 2 is a parent of vertex number 4.

While to find out the numbers of two children of this node,

we need to multiply i by 2, this gives us this node and

multiply i by 2 + 1 and this gives us this node.

And these two nodes are indeed children of vertex number 4, right?

And this is very convenient.

This allows us to store the whole complete binary tree just in an array.

So we do not need to store any links for

each vertex to its parent and to its two children.

So these links can be computed just on the fly.

Again to give a concrete example, assume that we are talking about vertex number 3.

So in this case i = 3.

To find out the number of its parent, we just divide i by 2 and round down.

So this gives us vertex number 1, and

indeed vertex number 1 is a parent of the vertex number 3.

And to find out the numbers of its two children,

we just multiply i by 2 and also multiply i by 2 and add 1.

This gives us, in this case, vertices number 6 and vertex number 7.

So, and we know its indices in theory.

Okay, we have just discussed two advantages of complete binary trees, and

it would be too optimistic to expect that these advantages come to us at no cost.

So we need to pay something, and our cost is that we need to keep the tree complete.

Right, we need to ensure that at each point of time,

our binary tree is complete.

Well, to ensure this, let's just ask ourselves

what operations change the shape of our tree.

Essentially, these are only two operations, namely insert and

extract max.

So, two operations, sift up and sift down,

they actually do not change the shape of the tree.

They just swap some two elements inside the tree.

Another operation which actually change the shape is remove an element,

however, it does so by calling the ExtractMax procedure.

So on the next slide we will explain how to modify our Insert and

ExtractMax operations so that they preserve completeness of our tree.

To keep a tree complete when we insert something into a complete binary tree,

we just insert the new element,

as a leaf, to the leftmost vacant position on the last level.

Well, an example is given here.

So, we insert element 30 just to the right of the last element on the last level.

Okay, then we need to let this new element sift up so we perform a number of swaps.

So 30 is swapped with 14, then still there is a problem, so

30 is greater than 29, so we swap it again.

Okay, now the property of the heap is satisfied for all the edges.

Well, when we need to extract the maximum value,

recall that we first replace the root by some leaf.

Well in this case, to keep the tree complete,

let's just select the last leaf at the last level.

In this case it is 14.

So we'll replace 42 with 14, and then, again,

perform a number of swaps required to satisfy the property of the heap.

Okay, so in this case, 14 is swapped with 30, and then 14 is swapped with 29.

This gives us a correct heap whose underlying tree is a complete binary tree.

Well so far, so good, we now know how to maintain the tree complete and

how to store it in an array.

In the next video we will show the full pseudocode of the binary

max heap data structure.

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