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Data Structures

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University of California San Diego

Data Structures

1782 classificações

Curso 2 de 6 em Specialization Data 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!

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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

Conheça os instrutores

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

In this video,

we provide the full pseudocode of the binary max heap data structure.

Here we will maintain the following three variables.

H is an array where our heap will stay.

MaxSize is the size of this array, and

at the same time, it is the maximum number of nodes in our heap.

And size is the actual size of our heap.

So size is always at most maxSize.

So let me give you an example.

In this case, we're given a heap of size 9.

And it is stored in the first nine cells of our array H whose size is 13.

In particular, you may notice that there are some values here, and

it is actually some garbage.

We just don't care about any values that stay to the right

of the position numbered 9.

So our heap occupies the first nine positions in the array.

Also let me emphasis once again that we store just the array H and

also variables size and maxSize.

So this tree is given to us implicitly.

Namely, for any node,

we can compute the number of its parent and the number of its two children.

And we can compute it and access the corresponding value in this array.

For example, if we have no number three, then we can compute

the index of its left child, which is 2 multiplied by 3.

So the value of its right child is 18.

These implementations showing how to find given a node i,

the index of the parent of i and two children of i.

So they just implement our formulas in a straightforward way.

To sift element i up, we do the following.

While this element is not the root, namely,

i is greater than 1, and while the value

of this node is greater than the value of its parent, we do the following.

We swap this element with his parent.

So this is done on this line.

And then we proceed with this new element.

I mean, we assign i to be equal to Parent of i and go back to this while loop,

and we do this until the property is satisfied.

To sift an element number i down, we first need to select the direction of sifting.

Namely, if element number i is smaller than one or two of its children,

we first need to select the largest one of its two children, right?

So this is done here.

So initially, we assign to the variable maxIndex the value of i.

Then we compute the index of the left child of the node number i.

Then in the next if loop we first check whether i indeed has a left child.

This is done in the following check.

We check whether l is at most size.

Namely, whether l is an index which is in our heap, okay?

Then if H of l is greater than H of maxIndex,

we assign maxIndex to be equal to l, okay?

Then we do the same with the right child.

We first compute its index, then we check whether this index is indeed in our heap.

Then we check whether the value in this node

is greater than the value of our current maximum index.

And if it is, then we update the value of maxIndex.

And finally, if the node i is not the largest one among itself and

the two of its children, namely,

if i is not equal to maxIndex, then we do the following.

We swap element number i with element number maxIndex.

It is done here.

And then we continue sifting down

the just-swapped element.

Okay, so, this is done recursively.

However, it is not so difficult to avoid using recursion here,

just by introducing a new while loop.

To insert a new element with priority p in our binary max heap, we do the following.

We first check whether we still have room for

a new element, namely whether size is equal to maxSize.

If it is equal, then we just return an error.

Otherwise, we do the following.

We increase size by 1, then we assign H of size to be equal to p.

At this point we add a new leaf in our

implicit tree to the last level, to the leftmost position on the last level.

And finally, we call SiftUp to sift this element up if needed.

To extract the maximum value from our binary max heap,

we first store the value of the root of our tree in the variable result.

So result is assigned to be equal H of 1.

Then we replace the root by the last leaf, by the rightmost leaf on the last level,

so this is done by assigning H of 1 to be equal to H of size.

Okay?

Then we decrease the value of size by 1, just to show that

the last leaf is not in our tree anymore.

And finally, we call SiftDown for the root, because it was replaced by

the last leaf, which is potentially quite small and needs to be sifted down.

And the last instruction in our pseudocode is, we return the result.

That means the value which was initially in the root of our tree.

Removing an element.

So as we've discussed already,

this actually boils down to calling two procedures that we already have.

Once again, to remove element number i, we do the following.

First, we change its priority to be equal to infinity, so

we assign H of i plus infinity.

Then we SiftUp this node, so this will move this node to the root of our tree.

And then we just call ExtractMax() procedure, which will remove the root from

this tree and make necessary changes in the tree to restore its shape.

Finally, to change the priority of a given node i to the given value p,

we do the following.

We first assign H of i to p, okay?

Then we check whether the new priority increased

is greater than the old priority or is smaller.

If it is greater, then potentially we need to sift up the new node.

So we just call sift up.

If the new priority is smaller, then we call SiftDown for this node.

Time to summarize.

In this sequence of videos, we considered the binary max heap data structure,

which is a popular way of implementing the priority queue data type.

The considered implementation is quite fast, all operations work

in logarithmic time and GetMax procedure works even in constant time.

It is also space efficient.

In this data structure, we store a tree, but this tree is stored implicitly.

Namely, for each node, we do not store a connection or a link to its parent and

its two children.

Instead, we compute the index of the corresponding nodes on the fly.

Well, in this case, we store just n given cells in an array, nothing more.

Okay? Another advantage of this data structure,

of this implementation, is that it is really easy to code.

As you've seen, the pseudocode of each operation is just a few lines of code.

Well, in the next video,

we will show how to use binary heap to sort data efficiently.

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