Implemented and Enhanced Activity Selection Algorithms

📅 2024-04-17 — Session: Implemented and Enhanced Activity Selection Algorithms

🕒 00:10–03:00
🏷️ Labels: Activity Selection, Algorithm Design, Verbose Output, Visualization, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to implement and enhance various activity selection algorithms using different approaches such as backtracking, dynamic programming, and greedy methods. The session also aimed to improve educational transparency by adding verbose outputs and visualizations.

Key Activities

• Mapped exercises to Chapter 5 of Kleinberg and Tardos’s Algorithm Design, focusing on divide and conquer techniques.
• Designed and implemented three versions of the activity selection algorithm using object-oriented programming: backtracking, dynamic programming, and greedy approaches.
• Enhanced the BacktrackingSelector, DynamicProgrammingSelector, and GreedySelector classes with verbose outputs to illustrate decision-making processes.
• Utilized matplotlib to visualize the activity selection process on a timeline, aiding in understanding and debugging.

Achievements

• Successfully implemented modular and reusable code for the activity selection algorithms.
• Improved educational value by providing clear visual and verbose outputs.

Pending Tasks

• Further testing and optimization of the implemented algorithms.
• Exploration of additional visualization techniques to enhance understanding.