🤖 Competitive Programming Solutions (C/C++)

This repository serves as a portfolio of my competitive programming journey and proficiency in algorithmic problem-solving using C and C++. It tracks my foundational training, dedicated study of core Data Structures, and implementation of various Graph and Optimization Algorithms.

Language	Total Problems Solved	Status
C/C++	204	Ongoing Development

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📁 Repository Structure and Focus Areas

The solutions are divided into three main categories, reflecting a sequential learning path:

Folder	Problem Count	Primary Focus
Initial_Training_Solutions	104	C/C++ syntax, basic implementation, mathematical logic.
Data_Structure_Training_Solutions	66	Foundational Data Structures and their efficient C++ implementation.
Algorithms_Training_Solutions	34	Core Graph, Search, and Optimization Algorithms.

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1. Initial Training Solutions (104 Files)

This folder contains the solutions from my initial training phase, primarily focused on establishing fluency in C and C++ syntax, control structures (loops, conditionals), and basic mathematical problem implementation.

• Goal: Master the core language features and translate beginner-level problems into functional code.
• Content Includes: Basic I/O, simple mathematical formulas, loops, and conditional logic problems.
• Organization: Files are generally organized chronologically by the order they were solved.

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2. Data Structure Training Solutions (66 Files)

This section demonstrates dedicated practice in implementing and utilizing essential data structures, which are foundational to efficient problem-solving.

• Goal: Deepen understanding of how to implement and choose the correct data structure for maximizing runtime efficiency.
• Key Data Structures Covered:
  • Singly Linked Lists
  • Doubly Linked Lists
  • Vectors/Dynamic Arrays
  • Queues and Priority Queues
  • Stacks
  • Binary Search Trees (BST)
  • Heaps
  • Sets and Maps

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3. Algorithms Training Solutions (34 Files)

This folder showcases proficiency in advanced algorithms necessary for competitive programming and complex engineering problems.

• Goal: Implement and apply standard graph traversal, shortest path, and optimization algorithms to solve problems.
• Key Algorithms Covered:
  • Shortest Path: Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd-Warshall Algorithm
  • Graph Traversal: Breadth-First Search (BFS), Depth-First Search (DFS)
  • Set Operations: Disjoint Set Union (DSU) / Union-Find
  • Optimization: Dynamic Programming (DP), Knapsack Problem Variations