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Singly-Linked Lists

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データ構造

カリフォルニア大学サンディエゴ校

4.7 (2,096 件の評価) | 91K在籍する学生

データ構造とアルゴリズム専門講座における 6 の 1 コース

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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学習するスキル

Binary Search Tree, Priority Queue, Hash Table, Stack (Abstract Data Type), List

レビュー

4.7 (2,096 件の評価)

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VK

Jul 16, 2018

Amazing course.\n\nInteresting exercises help with learning and understanding of internal mechanisms basic data structures, how some of them work, the pros and cons of each.\n\nThanks!

SC

Feb 27, 2019

Perfect course for learning more about fundamental data structures, except for presentations on few difficult topics like splay trees, where explanations can be made more elaborate!

レッスンから

Basic Data Structures

In this module, you will learn about the basic data structures used throughout the rest of this course. We start this module by looking in detail at the fundamental building blocks: arrays and linked lists. From there, we build up two important data structures: stacks and queues. Next, we look at trees: examples of how they’re used in Computer Science, how they’re implemented, and the various ways they can be traversed. Once you’ve completed this module, you will be able to implement any of these data structures, as well as have a solid understanding of the costs of the operations, as well as the tradeoffs involved in using each data structure.

Singly-Linked Lists9:02

Doubly-Linked Lists4:39

講師

Alexander S. Kulikov
Visiting Professor
Michael Levin
Lecturer
Daniel M Kane
Assistant Professor
Neil Rhodes
Adjunct Faculty

字幕

Now let's talk about linked lists.

So linked lists, it's named kind of like links in a chain, right, so

we've got a head pointer that points to a node that then has some data and

points to another node, points to another node and

eventually points to one that doesn't point any farther.

So here in our top diagram we show head points to the node containing 7,

points to the node containing 10, points to the node containing 4,

points to the node containing 13 doesn't point anywhere.

How this actually works is that a node contains a key which in

this case is these integers, and a next pointer.

The diagram below shows more detail of what's going on.

So head is a pointer that points to a node,

and that node contains two elements, the value 7.

And then a pointer that points off to the next node that contains a key 10,

and a pointer that points off to the next node 4,

points off to the next node 13, 13's next pointer is just nill.

What are the operations that can be done on a linked list?

There's several of them, and the names of these sometimes are different,

in different environments and different libraries.

But normally the operations provided are roughly these.

So we can add an element to the front of the list, and

that we're calling PushFront.

So that takes a key, adds it to the front of the list.

We can return the front element of the list.

We're calling that TopFront.

Or we can remove the front element of the list, called PopFront.

The same things that we can do at the front of the list,

we can also do at the end of the list.

With PushBack, later on in a later module,

we'll actually use the word Append for that, or TopBack, or PopBack.

These seem uniform in there, but there is a difference in that the runtimes

are going to be different between those, and we're going to talk about that.

You can find whether an element is in the list and it's as simple as just running

yourself down the the list looking to find a matching key.

You can erase an element and then again run yourself down the list til you

find the matching key and then remove that element.

So these latter ones are both O(n) time.

Is the list empty or not?

That's as simple as checking is the head equal to nil.

We can add a particular key--if we want to splice in a key into a list we can

actually add in a key either before a given node or after a given node.

So lets look at the times for some common operations.

We've got here our list with four elements in it:

7, 10, 4, and 13.

Now we go ahead and push an element to the front.

So we push 26 to the front of the list.

So the first thing we do, create a node that contains the 26 as its key.

And then we update our next pointer of that

node to point to the head, which is the 7 element, and

then update the head pointer to point to our new node, and that's it we're done.

So it's O(1).

Allocate, update one pointer, update another pointer, constant time.

If we want to pop the front element,

clearly finding the front element is very cheap here, right?

You can just look at the first element and return it.

So TopFront is O(1).

PopFront turns out is going to be O(1).

First thing we're going to do, update the head pointer.

Then, remove the node.

That's an O(1) operation.

If we want to push at the back, and we don't have a tail pointer,

we're going to talk about a tail pointer in a moment,

then it's going to be a fairly expensive operation.

We're going to have to start at the head and walk our way down the list until we

get to the end, and add a node there, so that's going to be O(n) time.

Similarly if we want to TopBack or PopBack, we're going to

also have to start at the head, walk our way down to the last element.

Those are all going to be O(n) time.

If we had a tail pointer, some of these will become simpler.

Okay, so, we're going to have both a head pointer that points to the head element

and a tail pointer that points to the tail element.

So, that way, getting the first element is cheap.

Getting the last element is cheap.

Let's look at what happens when we try an insert when we have a tail.

We allocate a node, put in our new key, and

we then update the next pointer of the current tail, to point to this new tail.

And then update the tail pointer itself.

O(1) operation.

Retrieving the last element, so a PopBack,

sorry a TopBack, is also an O(1) operation.

We just go to the tail, find the element, return the key.

If we want to pop the back however that's a little bit of an expensive operation.

Okay.

We are going to need to update the tail to point from 8 to 13 so

we're at 8 right now we want to go to 13, the problem is how do we get to 13?

Okay.

We don't have a pointer from 8 to 13 we have a pointer from 13 to 8.

And that pointer doesn't help us going back.

So what we've got to do is, again, start at the head, walk our way down until we find

the 13 node that then points to the current tail, and

then update our tail pointer to point to that, and

then update the next pointer to be nil.

And then we can remove that old one.

So that's going to be an O(n) operation.

because we've got to walk all the way down there.

Okay, because even though we have a tail pointer we don't have the next to the tail

pointer, we don't have the next to last element.

The head is different because our pointers point this way, if we had the head

Its also cheap to get the second element, right, and one more to get the third

element but the tail pointer doesn't help us get to the next to the last element.

Let's look at some of the code for this, so for PushFront we have a singly linked

list: we're going to allocate a new node, set its key, set its next

to point to the old head and then we'll update the current head pointer.

If the tail is equal to nil, that meant that before the insertion, the head and

the tail were nil, it was an empty list.

So we've got to update the tail to point to the same thing the head points to.

Popping the front, well,

if we're asked to pop the front on an empty list, that's an error.

So that's the first check we do here and

then we just update the head to point now to the head's next.

And just in case that there was only one element in the list and

now there are no elements, we check if our new head is nil and

if so update our tail to also be nil.

Pushing in the back: allocate a new node, set its key,

set its next pointer, and then check the current tail.

If the current tail is nil again, it's an empty list.

Update the head and the tail to point to that new node.

Otherwise update the old tail's next to point to our new node,

and then update the tail to point to that new node.

Popping the back.

More difficult, right.

If it's an empty list and we're trying to pop, that's an error.

If the head is equal to tail, that means we have one element.

So we need to just update the head and the tail to nil.

Otherwise we've got to start at the head, and start working our way down,

trying to find the next to the last element.

When we exit the while loop, p will be the next to last element, and

we then update its next pointer to nil.

And set our tail equal to that element.

Adding after a node?

Fairly simple in a singly linked list.

Allocate a new node,

set its next pointer to whatever node we're adding after, to its next.

So we sort of splice in, and then we need to update the node pointer.

The one we're adding after, so that it points now to our new node.

And just in case that node we're adding after was the tail we've got to now update

the tail to that new node.

Adding before, we have the same problem we had in terms of PopBack in that

we don't have a link back to the previous element.

So we have no way of updating its next pointer other than going

back to the beginning of the head and moving our way down until we find it.

So AddBefore would be an O(n) operation.

So let's summarize what the cost of things are.

PushFront, O(1).

TopFront, PopFront, all O(1).

Pushing the back O(n) unless we have a tail pointer in which case its O(1).

TopBack O(n), again unless we have a tail pointer in which it's O(1).

Popping the back: O(n) operation, with or without a tail.

Finding a key is O(n) we just walk our way through the list trying to find

a particular element.

Erasing, also O(n).

Checking whether it's empty or not is as simple as checking whether the head is nil.

Adding before: O(n) because finding the previous element

takes O(n) because we're going to walk all the way from the head to find it.

AddAfter: constant time.

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