Gain insights into array and linked list-based implementations, delve into advanced structures like skiplists and trees, and explore template-based collections for efficient data optimization and retrieval.

java.tar.gz

Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in C++


**Adjacency list representations** of graphs take a more vertex-centric approach. There are many possible implementations of adjacency lists. In this section, we present a simple one. At the end of the section, we discuss different possibilities. In an adjacency list representation, the graph $G = (V,E)$ is represented as an array, `adj`, of lists. The `adj[i]` list contains a list of all the vertices adjacent to vertex `i`. That is, it contains every index $j$ such that `(i,j)` $\in E$.

## Visual demonstration of the adjacency list

The visual demonstration of the adjacency list is shown below:

int n;
List < Integer > [] adj;
AdjacencyLists(int n0) {
  n = n0;
  adj = (List < Integer > []) new List[n];
  for (int i = 0; i < n; i++)
    adj[i] = new ArrayStack < Integer > ();
}

In this particular implementation, we represent each list in `adj` as an `ArrayStack`, because we would like constant time access by position. Other options are also possible. Specifically, we could have implemented `adj` as a `DLList`.

The `addEdge(i, j)` operation just appends the value `j` to the list `adj[i]`:

Learn about the representation of graphs by lists.

Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

AdjacencyLists: A Graph as a Collection of Lists