Path Compression

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Do curso por University of California San Diego

Data Structures

1774 classificações

University of California San Diego

Data Structures

1774 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.

Trees for Disjoint Sets7:54

Union by Rank9:41

Path Compression6:07

Analysis (Optional)18:17

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

To build our Y intuition involves a second characteristic for

the disjoint at the destruction.

Let's again consider the example shown here on the slide.

Assume that we call find of six.

This will traverse the following path from six to the root of this tree.

So let's know that in this case we find the root of three

that contains element six, but we also find the root

of the tree that contains element 12 and contains element three.

In general, by reversing this path we find the root for

all the elements on this path.

So, why lose this information?

Let's just store it somehow.

And one way to do this, for example,

is to re-attach all these notes directly to the root, we can do this as follows.

Now as you can see the parent of element 12 for

example is five, and also the parent of element six is also five.

We've just attached them directly to the root.

And this can not only save us space in the future.

Save us time, I'm sorry, in the future.

For future calls of find operation.

So this heuristic is called path compression.

Implementing this heuristic I mean path compression heuristic,

don't sound to be surprisingly simple.

It is actually only three lines of code.

Here we do the following.

We first check whether i is equal to parent of i.

If it is equal, if it is the root, then we will just return the result.

If i is not the root, if i is not the root, we do the following.

We call find recursively for the parent of the node i.

This is done here.

So we call find for the parent of the node i.

It will return the root of the correspondent three.

And then we set parent of i to be equal to the returned root.

That is, we attach the node number i directly to the root.

And we do this recursively for all the elements on this pass.

Finally we return the new parent of i,

which is now just the root of the corresponding tree.

Before stating an upper bound on the running time of operations of our current

implementations, we need to introduce the so-called iterated logarithm function,

which is also denoted by log star of n.

So by definition, iterated logarithm of n is the number of times the binary

logarithm should be applied to n before we get a number which is at most one.

Let me illustrate this with an example so

n equal to one is a binary logarithm of n is equal to zero.

We do not need to apply binary logarithm to get a number which is at most one,

because n is already almost one in this case.

For n equal to two,

we need to apply binary logarithm once to get the number which is at most one.

Mainly, if we apply binary algorithm to two we just get the number one.

Okay for n equals to three and four,

the binary algorithm is equal to two and so on.

And for example, for the numbers shown here,

two to the sixth, 5536 If we apply binary logarithm once,

then just by definition of binary logarithm we get this number,

just 65536, which is two to 16, okay?

So if we apply the binary logarithm once again we get 16, 16 is two to the four.

If we apply the binary logarithm once again we get four.

If we apply, again, we get two, and if we apply it finally once again, we get one.

And at this point, we stop.

So we applied the binary logarithm five times to get a number which is at

most one, which ensures that for this number,

two to the 65536 the log star is equal to five, okay?

So, and this shows that for any practical value of n, the binary log,

the log star function is, at most, five.

Because this number is extremely huge.

We will never see any value of

m which is greater than this number in practical value.

We will never get an input, a sequence that consists of so many elements.

So theoretically speaking, the lock star function is not bound.

It is not constant.

So there are extremely huge numbers for which lock star is equal to ten or

twenty or 100 and so on.

However, for all practical values of n, log star of n is at most five.

We're now ready to state an upper bound.

Assume that we used both union by rank heuristic and past compression heuristic,

and assume that we make m operations with our data structure,

of which m are calls to the MakeSet does a MakeSet operation.

Namely, we have n object, and we make m operations with them.

Then the total running time of all these calls is O(mlog*n).

Put it otherwise, the amortized running time of a single operation is O(log*n).

And recalls it for all practical values of n, log star of n is at most five.

Which means that we have a constant average time for

a single operation for all practical values of n.

So once again, log star theoretically speaking,

log star function is not bound, however it is at most five for

all practical values of n which makes our data structure very efficient in practice.

We will prove this upper bound in the next video.

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