📅 2023-08-19 — Session: Developed and Analyzed Greedy Pairing Algorithm
🕒 18:55–19:30
🏷️ Labels: Python, Greedy Algorithm, Backtracking, Optimization, Algorithm Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and analyze a greedy algorithm for pairing dancers based on their skills and weights, and to compare its optimality against a backtracking approach.
Key Activities
- Implemented a greedy algorithm in Python to pair dancers efficiently.
- Conducted an analysis of the algorithm’s optimality and compared it with backtracking solutions.
- Streamlined the algorithm code for clarity and efficiency.
- Introduced the concept of
suma_minima_costousing a heap data structure for cost minimization problems.
Achievements
- Successfully implemented and tested a greedy algorithm that paired dancers optimally on the first attempt.
- Simplified the algorithm code by removing unnecessary comments and focusing on core functionality.
- Laid groundwork for future comparisons with backtracking methods to further validate the solution’s optimality.
Pending Tasks
- Conduct a detailed comparison between the greedy algorithm and backtracking approach to fully assess optimality.
- Explore further applications of the
suma_minima_costofunction in different problem domains.