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HomeBlogWeb DevelopmentBasics of Data Structures and Algorithms in C++
Understanding data structures and algorithms (DSA) in C++ is key for writing efficient and optimised code. Some basic DSA in C++ that every programmer should know include arrays, linked lists, stacks, queues, trees, graphs, sorting algorithms like quicksort and merge sort, and search algorithms like binary search.
Arrays allow storing data elements of the same data type sequentially in memory. Linked lists consist of nodes that connect to each other through references. Stacks are linear data structures that follow a LIFO (Last in First out) order. Queues follow a FIFO (First in First out) order. Trees and graphs enable representing hierarchical relationships between objects. Sorting algorithms like quicksort and merge sort allow arranging data elements in a specific order efficiently. Search algorithms like binary search help locate an element in a dataset very quickly. Data structure training is helpful to learn data structures and algorithms in C++.
Knowing these basic DSA in C++ with examples helps in writing better programs that leverage optimised data structures and algorithms.
In this article, we will go through a number of data structures and learn about algorithms to create the foundation of data structure and algorithm in C++. This foundation is useful for tackling more complex programming problems and developing efficient software applications. So, let’s deep dive to understand what is DSA in C++?
Data structures are basically different formats for organising information on a computer. It's kind of like how you might organise things in your home—using boxes, folders, shelves etc. Some types of data structures in C++ that programmers use are arrays, linked lists, stacks and so on. They allow the computer to store and access data efficiently.
Algorithms are like step-by-step recipes or instructions for doing calculations, sorting information, searching for things and other tasks. They use the data structures to organise and work with the information in ways that make sense. For example, if you wanted to alphabetize a big list of words, you'd probably come up with a system—maybe break it into smaller groups in a clever way. That's similar to how sorting algorithms like quicksort work—using different data organisation tactics to improve speed and efficiency.
Now-a-days, developers use both DSA using C++ and online Web Development certificate to stand out of the crowd, that’s why it becomes critical to understand a programming language like C++ with DSA.
DSA with C++ is crucial now-a-days due to the competitive tech market. To excel in software development, mastering data structures and algorithm analysis in C++ is crucial. Not only understanding the concepts help you create better software, but the product would also be efficient and robust. There is limitless importance of DSA in C++, out of which, some are as follows:
Computers have special structures they use to store and organise information—almost like different shaped containers you might keep stuff in at home! A few examples of data structures using C++ are arrays (neat rows of boxes for numbers or words), linked lists (jumbled boxes connected by arrows), stacks (tall towers where you can only reach the top box), queues (straight lines where the first one in is the first one out), trees (branching vine maps that show connections), and graphs (dot maps with lines between dots). Keep reading to learn through easy examples what each data structure does, also you can enrol yourself into the Knowledgehut's Software Engineering courses after graduation to get more deeper insights about development. Let’s move forward to learn C++ data structures.
Think of an array as a row of numbered boxes. You can store one thing in each box and keep track of what's in each one by its number. So, if you have 5 game controllers, you could put one in each of 5 array boxes to remember which is which.
In C++, arrays can be declared statically using a fixed size or dynamically by allocating memory. Accessing elements uses index notation like array [2].
Data structure programs in C++ with output:
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#include <iostream>
using namespace std;
int main() {
int arr[8] = {4, 7, 2, 9, 1, 6, 8, 3};
int max = INT_MIN;
for (int i = 0; i < 8; ++i) {
if (arr[i] > max) {
max = arr[i];
}
}
cout << "Max element: " << max << endl;
return 0;
}Output: Max element: 9
Next in the list of examples of data structures in C++ is Linked List. A linked list is like a scavenger hunt, except instead of clues, each stop on the hunt has the address of the next stop. You start with the first stop, then go to the address written there to find the next stop and so on. Computers can store data pieces in this order using addresses.
Linked lists contain separate objects called nodes that connect to each other through pointers. In C++, a node contains data and a pointer to the next node location. Linked lists can grow organically by allocating new nodes dynamically and connecting them. Traversal uses pointer chasing through the node network.
Stacks are like piles of dirty dishes that keep getting taller as you add more. You can only take something from the very top dish in the stack. Then when you add another dish, it goes to the new top. Computer stacks work just like this too with data instead of dishes!
Stacks follow a strict LIFO (Last In First Out) structure where elements are added and removed from the same end. In C++ stacks are easily built using templates and vector containers. The push() and pop() functions add and remove elements respectively by manipulating a top pointer.
A queue works more like a checkout line at the grocery store. The first one in line gets checked out first. New things that join go to the back. First in, first out for computers too.
Queues are FIFO (First In First Out) linear structures where elements are inserted at the back and removed from the front. C++ queues are commonly implemented using linked lists or as queue adaptors to std::deque containers. The enqueue() and dequeue() functions handle insertion and removal.
Family trees show how people in a family are related. Computer tree data structures work similarly by having connected branches of parent-to-child information in a branching hierarchy.
Trees arrange data hierarchically, linking child nodes to parent nodes like branches. C++ provides tree templates/containers and traversal algorithms like preorder, inorder, postorder to access nodes. Nodes contain left/right pointers to children and data. Depth and balancing are important tree properties.
Charts that connect points together with lines are called graphs. Computer graphs also have points (called nodes) with connections between them according to special rules, almost like a map!
Graphs consist of nodes/vertices connected by edges representing relationships. C++ graphs use adjacency list/matrix representation. The vertices hold data while edges depict connections. Graph algorithms like search, shortest path, and spanning tree run complex analysis.
There are neat tricks for sorting things faster, like Quick sort and Merge sort. Quick sort works by splitting a messy pile in half over and over until the piles are sorted. Like if you and your sibling each sorted half the pile of toys. Merge sort first divides toys individually, then has you and your sibling combine your small, sorted piles together into bigger sorted piles.
Implementation of Quick Sort:
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#include <iostream>
#include <vector>
using namespace std;
// Function to partition the array and return the pivot index
int partition(vector<int>& arr, int low, int high) {
int pivot = arr[high];
int i = low - 1;
for (int j = low; j < high; j++) {
if (arr[j] < pivot) {
i++;
swap(arr[i], arr[j]);
}
}
swap(arr[i + 1], arr[high]);
return i + 1;
}
// Function to perform Quick Sort recursively
void quickSort(vector<int>& arr, int low, int high) {
if (low < high) {
int pivotIndex = partition(arr, low, high);
quickSort(arr, low, pivotIndex - 1);
quickSort(arr, pivotIndex + 1, high);
}
}
// Function to print the array
void printArray(const vector<int>& arr) {
for (int num : arr) {
cout << num << " ";
}
cout << endl;
}
int main() {
vector<int> arr = {12, 11, 13, 5, 6, 7};
int n = arr.size();
cout << "Original array: ";
printArray(arr);
quickSort(arr, 0, n - 1);
cout << "Sorted array: ";Hunting through a huge messy pile can take forever! So smart searching algorithms like Binary Search help computers lookup information much quicker. Binary search works by first checking the middle item. If it's not what you want, it knows to only search either the left or right half. It's way faster than searching a giant pile item by item, that’s the beauty of learning DSA in C++.
Implementation of Binary Search:
#include <iostream>
#include <vector>
using namespace std;
// Function to perform binary search
int binarySearch(const vector<int>& arr, int target) {
int left = 0;
int right = arr.size() - 1;
while (left <= right) {
int mid = left + (right - left) / 2;
// Check if target is present at mid
if (arr[mid] == target)
return mid;
// If target is greater, ignore left half
else if (arr[mid] < target)
left = mid + 1;
// If target is smaller, ignore right half
else
right = mid - 1;
}
// If target is not present in array
return -1;
}
int main() {
vector<int> arr = {1, 2, 3, 4, 5, 6, 7, 8, 9};
int target = 5;
int result = binarySearch(arr, target);
if (result != -1)
cout << "Element found at index " << result << endl;
else
cout << "Element not found in the array" << endl;
return 0;
}Algorithms for graphs (point maps with lines between them) include Dijkstra's Shortest Path. It lets the computer find the quickest way to get from one point to another based on the connecting lines, as if figuring out the fastest route for you to get to the new skatepark!
Implementation of Dijkstra’s algorithm
#include <iostream>
#include <vector>
#include <queue>
#include <limits>
using namespace std;
// Define a structure to represent a weighted edge
struct Edge {
int to;
int weight;
Edge(int t, int w) : to(t), weight(w) {}
};
// Define a structure to represent a vertex
struct Vertex {
vector<Edge> edges;
};
// Function to perform Dijkstra's algorithm
vector<int> dijkstra(const vector<vector<Vertex>>& graph, int start) {
int n = graph.size(); // Number of vertices
vector<int> dist(n, numeric_limits<int>::max()); // Initialize distances to infinity
vector<bool> visited(n, false); // Mark all vertices as not visited
dist[start] = 0; // Distance from start vertex to itself is 0
priority_queue<pair<int, int>, vector<pair<int, int>>, greater<pair<int, int>>> pq;
pq.push({0, start}); // Push the start vertex into the priority queue
while (!pq.empty()) {
int u = pq.top().second;
pq.pop();
if (visited[u]) continue;
visited[u] = true;
for (const Edge& e : graph[u].edges) {
int v = e.to;
int weight = e.weight;
if (dist[u] != numeric_limits<int>::max() && dist[u] + weight < dist[v]) {
dist[v] = dist[u] + weight;
pq.push({dist[v], v});
}
}
}
return dist;
}
int main() {
// Example graph represented using an adjacency list
vector<vector<Vertex>> graph = {
{{Edge(1, 4), Edge(2, 1)}}, // 0
{{Edge(3, 2)}}, // 1
{{Edge(1, 2), Edge(3, 5)}}, // 2
{{Edge(4, 3)}}, // 3
{} // 4
};
int startVertex = 0;
vector<int> shortestDistances = dijkstra(graph, startVertex);
cout << "Shortest distances from vertex " << startVertex << ":\n";
for (int i = 0; i < shortestDistances.size(); ++i) {
cout << "Vertex " << i << ": ";
if (shortestDistances[i] == numeric_limits<int>::max()) {
cout << "INF\n";
} else {
cout << shortestDistances[i] << "\n";
}
}
return 0;
}So, you know how C++ lets you build all kinds of computer programs for games, apps, visual stuff, anything really...which is super cool! But to really make your programs work smoothly and fast, you need to structure and organise all that data and instructions in smart ways.
C++ gives you so much flexibility already—it's insanely powerful. But if you just stick to the basics, it's kinda like having the latest smartphone and only using it to make calls. You're missing out on a lot more! By taking time to master core data organisation tools like arrays, lists, stacks and handy algorithms that make tasks radically easier, you can build way cooler things. DSA in C++ hence become crucial to learn. It lets you write apps, games, and visual programs that truly leverage C++’s native speed and run slick and efficient.
So, for anyone excited to unlock C++’s full potential by learning DSA in C++, diving into those data structures and algorithms is absolutely worth the effort. That’s what takes your programming to the next level. The language keeps evolving but that fundamental set of knowledge stands strong. Learn it well!
Arrays in C++ have elements stored sequentially in memory, allowing random access but limited insertions/deletions. Linked lists store elements separately as objects with pointers to the next node, making insertions/deletions easier but only sequential access is allowed.
Algorithms are like smart shortcuts for complex data tasks in C++. Rather than spelling out every little step, they package up sequences of optimised instructions to sort stuff, find things quick, make super fast calculations—all useful tricks for writing slick programs!
Arrays in C++ store data elements of the same type sequentially allocated in contiguous memory allowing indexed access, so elements can be efficiently accessed directly by position, but arrays have fixed size declared initially limiting expandability during execution.
Basic searching in C++ can be done sequentially iterating through data structures checking each element for matching criteria in linear time, but for faster searches of sorted data, binary search and lower_bound/upper_bound algorithms from standard libraries provide log time search.
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