Skip to content

SomtoOnyekwelu/algorithmic-problem-solving

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 

Repository files navigation

Algorithmic Problem Solving Practice (GSoC Prep)

Applicant: Somto Onyekwelu (https://github.com/SomtoOnyekwelu)

Purpose

This repository contains my solutions to selected Data Structures and Algorithms problems from platforms like LeetCode. This practice is part of my preparation for technical interviews and potential contributions, demonstrating my problem-solving abilities and understanding of core computer science concepts.

The goal is to showcase clean, well-commented code that correctly implements standard algorithms and data structures for common problem patterns.

Solved Problems

  1. LeetCode 1038: Binary Search Tree to Greater Sum Tree (Problem Link)

    • File: bstToGst.py (View Code)
    • Concepts Demonstrated: Binary Search Trees, Tree Traversals (Reverse In-Order DFS), Recursion, In-place modification.
    • Complexity: O(N) Time, O(H) Space (where H is tree height).
  2. LeetCode 200: Number of Islands (Problem Link)

    • File: numIslands.py (View Code)
    • Concepts Demonstrated: 2D Grid Traversal, Graph Algorithms (DFS), Recursion, Managing Visited States (in-place modification).
    • Complexity: O(MN) Time, O(MN) Space (worst-case recursion depth).

Will potentially add more problems focusing on other core DS&A like Heaps, HashMaps, Sliding Window etc. as practice continues.

Running Examples

Example usage sections are included within an if __name__ == "__main__": block in the individual Python files for basic verification.

Connection to GSoC Proposals

While these are general DS&A problems, the skills demonstrated – algorithmic thinking, efficient coding in Python, understanding time/space complexity, and clean implementation – are directly relevant to the technical challenges involved in my GSoC proposals targeting Google DeepMind (Gemini Robustness Evaluation) and NumFOCUS/aeon (ML Forecasting Evaluation).

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published