Explore
For Enterprise
Join for Free
Log In

Rutgers University
Become a skilled supply chain leader in 4 months
Earn the Supply Chain Excellence MasterTrack™ Certificate from a top-ranked business school.
HEC Paris
Register for HEC Paris' upcoming webinar
Join HEC's webinar to learn more about the MSc in Innovation and Entrepreneurship.

Explore todo o Coursera

Navegar
Os melhores cursos
For Enterprise
Entrar
Inscreva-se gratuitamente

Final Remarks

Loading...

estrutura de dadosestrutura de dados

Universidade da Califórnia, San Diego

4.7 (2,504 classificações) | 100K alunos inscritos

Curso 2 de 6 no Estruturas de dados e algoritmos Programa de cursos integrados

Cadastre-se gratuitamente

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! Do you have technical problems? Write to us: coursera@hse.ru

Visualizar o programa do curso

Habilidades que você aprenderá

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

Avaliações

4.7 (2,504 classificações)

5 stars
1,846 ratings
4 stars
528 ratings
3 stars
90 ratings
2 stars
15 ratings
1 star
25 ratings

AS

Nov 23, 2019

The lectures and the reading material were great. The assignments are challenging and require thought before attempting. The forums were really useful when I got stuck with the assignments

SG

Oct 28, 2019

I found the course a little tough, but it's worth the effort. It takes more time than mentioned. Apart from that, it is actually good and covers most of the topics required for interviews.

Na lição

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.

Building a Heap10:34

Final Remarks4:02

Ministrado por

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

Experimente o curso Gratuito

Transcrição

We'll conclude the lesson by a few remarks. First of all, for implementing a binary heap in an array, we can as well use zero based arrays. In this case, the formulas for computing the parent and the index of the parent of two children of a given node, I changed as follows. So, the Parent of i, is given by the number (i-1)/2 and rounded down. The LeftChild is given by the number 2i + 1 and the RightChild is given by 2i + 2. The next remark is that you can implement the binary min-heap in exactly the same way. And binary min-heap is a heap where each edge is value of their parent, is at most the value of the child. A case like this is useful for the case when a iteration just in your priority queue, you need to extract an element not with maximum priority, but with minimum priority. The final remark is that binary heaps can be easily generalized through d-ary heap. In such a heap, each node has, at most, d children. And we require to call a d-ary heap complete, we again require that all the levels are completely filled, except for possibly the last one where all the nodes are in leftmost position, okay? So the height of this tree is in this case, log base d of n, not the binary log of n. This in particular means that the running time of the SiftUp procedure is, at most, O of log base d of n, right? Just because the height of the tree is, at most, log base d of n. And the element just goes down, just goes up. If needed, we swap an element with its pairing. However, the running time of SiftDown procedure increases through d when supplied by a log base u of n. This is because when we go down, we always need to find a direction where to go in reach of this to go. And this is because, when we need to replace, to swap a node with one of its children we first need to select which on of these children is the largest one. Right, so for this reason the running time of SiftDown procedure in this case is O of d multiplied by log base d of n. Okay, time to conclude in this segment of lessons we started by introducing the abstract data type called priority cues. This abstract data types supports the following two main operations insert an element and extract an element with the highest priority. This priority queues find a lot of applications. We will see many other reasons that you used efficiently this data type. Then we explain that if implemented naively using an array, or at least sort it or not, one of these two operations will take a lenient amount of work, in the worst case. Then we presented binary heaps. So this is a way of implement priority queues that gives the worst case running time, O of log n for all operations. And also, finally, we explained that this also can be made space efficient. Namely, a binary heap is a tree, however to store this tree we do not need you to store connections to a parent and to children. It is enough to store everything in an array. Again, this makes binary heaps both time and space efficient.

Explore nosso catálogo

Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.

Coursera

Sobre
Liderança
Carreiras
Lista de cursos
Certificados
Certificados MasterTrack™
Notas
For Enterprise
For Government
For Campus

Comunidade

Aprendizes
Parceiros
Desenvolvedores
Testadores beta
Tradutores
Blog
Tech Blog

Mais

Termos
Privacidade
Ajuda
Acessibilidade
Imprensa
Contato
Diretório
Afiliados

Aprenda em qualquer lugar

Siga-nos

© 2020 Coursera Inc. Todos os direitos reservados.