Data Structures and Algorithms

This repository serves as a collection of my studies in data structures and algorithms. Each folder corresponds to a specific topic, containing both theoretical explanations and practical implementations.

Table of Contents

1. Introduction
2. Folder Structure
3. Implemented Data Structures and Algorithms
4. Solved Problems
5. Materials
6. Competitive Programming
7. Contributing

Introduction

In this repository, I've documented my journey of learning data structures and algorithms. My goal is to provide clear explanations, implementations, and real-world problem-solving examples.

Folder Structure

• Each folder represents a distinct data structure or algorithm.
• Inside each folder, you'll find:
  • implementation: Source code with explanations for the implementation of the data structure or algorithm.
  • problems: Solutions to problems related to the respective data structure or algorithm, often sourced from platforms like LeetCode.
  • materials: Educational resources, references, and learning materials related to the specific data structure or algorithm.

Implemented Data Structures and Algorithms

• Vector : Implementing a generic vector data structure using dynamic allocation of array and it's support these functionality

  • implementation:
    • size() : Number of elements.
    • capacity() : Number of elements it can hold.
    • empty() : Check if the vector is empty.
    • at(index) : return element at index, throw exception if index is out of bounds.
    • push_back(element) : push element at the end of the vector.
    • insert(element, index) : insert item at a given index and shift that index's value and trailing elements to the right (throw exception if index is out of bounds).
    • pop_back() : remove the last element.
    • erase(index) : remove element at a given index and shift all trailing elements to the left (throw exception if index is out of bound).
    • erase(element) : remove all elements that equal this given element and shift all trailing elements to the left.
    • find(element) : Looks for value and returns first index with that value, -1 if not found.
    • front() : return the first element (throw exception if vector is empty).
    • back() : return the last element (throw exception if vector is empty).

• Singly Linked List: Implementation of a generic singly linked list using pointers and dynamic allocation.

• Doubly Linked List: Implementation of a generic doubly linked list using pointers and dynamic allocation.

  • Two Data structure support the same functionality but different implementations.
  • implementation:
    • size() : returns number of data elements in list.
    • empty() - check if the linked list is empty or not (returns bool).
    • valueAt(index) : return the value of the nth item (0 based index).
    • push_front(element) : Adding element to the front of the list.
    • pop_front() : Removing element from the front of the list.
    • push_back() : Adding element to the back of the list.
    • pop_back() : Removing element from the back of the list.
    • front() : return the first element of the list.
    • back() : return the last element of the list.
    • insert(index, element) : Reach the given index at the list and insert the given element with considering the links between the pointers and fix them.
    • erase(index) : Remove element from the list at the given index.
    • valueNfromEnd(n) : returns the value of the node at nth position from the end of the list.
    • reverse() : Reversing the whole list. erase(element) : Remove the first element in the list with this value.
    • getValue(index) : Get the value from the node at this index.

• Hash Table (Chaining Method): Implementation of a generic hash table with key and value using chaining method.

• Hash Table (Open Addressing Method): Implementation of a generic hash table with key and value using open addressing method.

  • Two Data Structure support the same functionality but different implementations.
  • implementation:
    • add(key, value) : Add the key and value to the hash table.
    • exist(key) : Check if this key exists in the hash table.
    • get(key) : Get the value of the key.
    • remove(key) : Remove this key from the hash table.

• Binary Search Tree: Implementation of a generic Binary Search Tree.

  • implementation:
    • insert(element) : Insert new element into tree.
    • count(element) : Count the number of items equal to this element in the tree.
    • clear() : Clear all the tree.
    • find(element) : return true if the element exists in the tree otherwise return false.
    • getHeight() : Return the height of the tree.
    • getMin() : Return the minimum number of items in the tree.
    • getMax() : Return the maximum number of items in the tree.
    • isBinarySearchTree() : Check if this is a binary search tree or not.
    • erase(element) : delete all the items equal to this element.
    • print() : Print the tree in any ordered (Pre-Order, In-Order, Post-Order).

• Binary Heap: Implementation of a generic Binary Heap, Trying to simulate Priority_Queue and it's finctionality using vector as the container for the Priority_Queue and making the comparator as a generic type so you can use any comparator you want according to your usage and satisfies the heap property.

  • implementation:
    • empty() : Check if the Priority_Queue is empty or not.
    • size() : Return the size of the Priority_Queue.
    • push(element) : Push element to the Priority_Queue.
    • pop() : Pop the root element from the Priority_Queue.
    • top() : Return the root element of the Priority_Queue.

• Stack (Array Based Implementation): Implementation of a generic stack (Array Based Implementation) using dynamic allocation of array.

• Stack (Linked List Based Implementation): Implementation of a generic stack (Linked List Based Implementation) using pointers dynamic allocation.

  • Two Data Structure support the same functionality but different implementations.
  • implementation:
    • size() : returns number of data elements in stack.
    • empty() : Check if the stack is empty or not.
    • push(element) : Addint element at the top of the stack.
    • pop() : pop the top element of the stack.
    • top() : return the top element of the stack.

• Queue (Circular Array Base Implementation): Implementation of a generic Queue (Circular Array) using a fixed size array.

• Queue (Linked List Base Implementation): Implementation of a generic Queue (Linked List Base Implementation) using pointers and dynamic allocation.

  • Two Data Structure support the same functionality but different implementations.
  • implementation:
    • size() : return the number of elements in the queue.
    • empty() : check if the queue is empty or not.
    • full() : check if the queue is full or not.
    • enqueue() : adds item at end of available storage.
    • dequeue() : remove least recently added item from the queue.
    • front() : return the first element from the queue.
    • back() : return the last element from the queue.

• Sorting

  • Implementation of different Sorting algorithms and trying different ways of implementations.

Solved Problems

This section includes solutions to various algorithmic problems sourced from platforms like LeetCode and HackerEarth. Each problem is accompanied by the implemented solution. These solutions demonstrate practical applications of the data structures and algorithms discussed in this repository.

• Array & Vector

• Linked Lists

• Hash Table

• Binary Tree

• Binary Heap

• Stack

• Queue

• Sorting

Materials

This section contains educational materials and resources that have been instrumental in my study of data structures and algorithms.

• Array & Vector

• Linked List

• Hash Table

• Binary Tree

• Binary Heap

• Stack

• Queue

• Sorting

Competitive Programming

This is not just my journey with data structure, algorithms and problem solving, I had a long journey with competitive programming, which i will explain it in the following section below.

Welcome to the Competitive Programming section of this repository! Competitive programming is a fascinating domain that combines problem-solving skills with programming prowess. In this section, I'll share insights into my journey in competitive programming, highlighting achievements and experiences gained along the way.

Introduction

Competitive programming involves solving algorithmic challenges and computational problems within a specified time frame. It's not just about writing code; it's about crafting efficient solutions under time constraints. Whether you're a seasoned competitor or just starting, the world of competitive programming offers a unique opportunity to hone your skills, learn new techniques, and engage with a vibrant community of like-minded individuals.

My Achievements

Over the journey of my competitive programming, I've had the privilege of achieving milestones and gaining valuable experiences. Here are some notable achievements:

• I have participated in ICPC competitions several times, you can find my ICPC profile here Muhamed Morsi, where I proudly showcase my achievements and journey in the International Collegiate Programming Contest (ICPC).

  • What is ICPC ?

    • The International Collegiate Programming Contest (ICPC) is a premier global programming competition where university students from around the world engage in solving algorithmic challenges. It provides a platform to showcase problem-solving skills, teamwork, and programming proficiency. The thrill of tackling diverse problem sets and the invaluable lessons learned during these competitions significantly contribute to the growth of participants as programmers.

• I have reached the Specialist(Almost Expert) rank on Codeforces, a testament to my dedication, perseverance, and love for algorithmic problem-solving. With a track record of participation in over 130 online contests on Codeforces, I've successfully tackled and conquered more than +2500 problems, spanning various difficulty levels and employing a diverse range of data structures and algorithms. You can explore my Codeforces profile here Muhamed_Morsi, where the journey from a novice to a Specialist unfolds.

  • What is Codeforces ?

    • Codeforces stands as a premier online competitive programming platform, hosting regular contests that challenge programmers worldwide.

All my profiles at the platforms I have practiced problems on :

• LeetCode
• Codeforces
• AtCoder
• HackerEarth
• HackerRank

Contributing

If you'd like to contribute, have suggestions for improvements or find any wrongs in the implementations of data structures feel free to open an issue or submit a pull request. Contributions are welcome!