A systematic, living library of core computer science data structures and algorithmic patterns, optimized for high-performance execution.
The objective of this repository is to bypass brute-force logic in favor of mathematically and structurally optimized solutions. Every implementation prioritizes strict management of Time Complexity (Big O) and Space Complexity using industry-standard algorithmic patterns.
All code is written in Python, adhering to strict type-hinting and PEP-8 clean code standards.
This repository serves as a proof-of-work for the following data manipulation techniques:
-
Hash Maps & Frequency Counters: Trading memory for
$O(1)$ constant-time lookups to eliminate nested loops. -
Two-Pointer Techniques: Manipulating arrays and strings from multiple indices simultaneously for
$O(1)$ in-place memory optimization. - Sliding Windows: Dynamically adjusting subset bounds to optimize contiguous subarray problems.
- Graph Traversal: Implementing Breadth-First and Depth-First Search for complex network routing.
- Dynamic Programming: Utilizing memoization and tabulation to break down overlapping subproblems.