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"Two-Pointers-with-Himel" repository offers implementations and explanations of the Two Pointer Technique, a powerful algorithmic approach for efficient problem-solving in arrays and linked lists. Explore Python code examples, detailed problem analyses, and contributions to enhance your understanding and mastery of this technique.

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Two-Pointers-with-Himel

This repository contains code implementations and explanations of the Two Pointer Technique, curated by Himel. The Two Pointer Technique is a popular algorithmic approach used to solve problems efficiently, particularly in arrays and linked lists.

What is the Two Pointer Technique?

The Two Pointer Technique is a strategy used to solve problems that involve iterating through an array or a linked list with two pointers simultaneously. These pointers usually start from opposite ends of the data structure or at different positions and move towards each other or in different directions based on the problem's requirements.

Contents

  • Code Implementations: This section contains Python code examples demonstrating various applications of the Two Pointer Technique.

  • Explanation and Analysis: Detailed explanations and analyses of the problems solved using the Two Pointer Technique. Each problem is accompanied by an explanation of the approach, time and space complexity analysis, and possible variations.

How to Use

  • Clone the repository to your local machine using git clone.
  • Navigate to the desired code implementation or explanation folder.
  • Open the Python files or Markdown documents to view the code implementations and explanations.
  • Run the Python scripts to see the code in action.

Contributions

Contributions to this repository are welcome! If you have a new problem solution or an improvement to an existing one, feel free to submit a pull request. Please ensure that your code is well-documented and follows the repository's code style.

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"Two-Pointers-with-Himel" repository offers implementations and explanations of the Two Pointer Technique, a powerful algorithmic approach for efficient problem-solving in arrays and linked lists. Explore Python code examples, detailed problem analyses, and contributions to enhance your understanding and mastery of this technique.

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