📅 2023-11-16 — Session: Optimized academic research and algorithmic strategies
🕒 16:00–18:00
🏷️ Labels: Optimization, Academic Research, Algorithms, Graph Theory, Data Science
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to align research papers with institutional specializations and explore algorithmic strategies for optimization problems in academic settings.
Key Activities:
- Analyzed how the research paper ‘Debunking Granularity: A Comprehensive Analysis of Aggregate Volatility’ aligns with institutional research groups, highlighting relevant departments and their interests.
- Provided a structured summary of the paper’s main theme, methodology, structure, and institutional relevance.
- Explored various algorithmic approaches for optimizing course scheduling, including graph theory, linear programming, rule-based systems, genetic algorithms, and classification algorithms.
- Developed strategies for optimizing correlativity modeling in directed graphs using adjacency matrices, topological sorting, shortest paths, quadratic optimization, strongly connected components analysis, clustering techniques, and heuristic methods.
- Formulated a formal approach to minimize discrepancies in level assignments for courses, detailing steps for variable definition, objective function formulation, constraint establishment, problem-solving, and result adjustment.
- Implemented a step-by-step approach to propagate hard correlativities in a Pandas DataFrame using NetworkX and the shortest path algorithm, including a code outline.
Achievements:
- Successfully aligned research papers with institutional specializations and explored comprehensive algorithmic strategies for optimization problems.
Pending Tasks:
- Further refinement of the optimization strategies and implementation of the proposed algorithms in real-world scenarios.