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Accepted solutions, doing topics on: Introductory Problems, Sorting and Searching, Dynamic Programming, Graph Algorithms, Range Queries, Tree Algorithms, Mathematics, String Algorithms, Geometry.

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DoanTrungHuy/exercises-and-solutions-to-algorithmic-problems

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Exercises and Solutions to Algorithmic Problems

This repository contains a curated collection of algorithmic problems along with their accepted solutions. The problems are organized into key topics that cover a wide range of algorithmic challenges. Whether you're a beginner or looking to deepen your understanding of algorithms, this repository provides a comprehensive resource for practicing and mastering various algorithms.

Topics Covered

The exercises are categorized into the following core topics:

1. Introductory Problems

  • Fundamental problems designed for beginners to get started with basic algorithmic techniques.

2. Sorting and Searching

  • Various sorting algorithms (Quick Sort, Merge Sort, etc.) and searching techniques (Binary Search, Linear Search, etc.).

3. Dynamic Programming

  • Classic dynamic programming problems that teach you how to optimize solutions by solving subproblems and storing intermediate results.

4. Graph Algorithms

  • Algorithms to solve graph-based problems, including traversal techniques like DFS, BFS, and algorithms for shortest paths, minimum spanning trees, and more.

5. Range Queries

  • Problems that deal with querying ranges of data efficiently, such as segment trees and binary indexed trees.

6. Tree Algorithms

  • Problems involving binary trees, binary search trees, AVL trees, and various tree traversal algorithms.

7. Mathematics

  • Algorithmic problems focused on mathematical concepts such as number theory, combinatorics, and prime factorization.

8. String Algorithms

  • A collection of problems related to string manipulation, pattern matching, and string processing algorithms like KMP and Rabin-Karp.

9. Geometry

  • Algorithms related to computational geometry, including problems on points, lines, and polygons.

Features

  • Well-documented solutions: Each problem is accompanied by a detailed explanation of the approach, time complexity, and edge cases.
  • Optimized solutions: Solutions are implemented with an emphasis on efficiency and scalability.
  • Comprehensive test cases: Every solution is tested with multiple test cases to ensure correctness.

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Accepted solutions, doing topics on: Introductory Problems, Sorting and Searching, Dynamic Programming, Graph Algorithms, Range Queries, Tree Algorithms, Mathematics, String Algorithms, Geometry.

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