Optimized Course Assignment Using Graph Algorithms

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📅 2023-11-16 — Session: Optimized Course Assignment Using Graph Algorithms

🕒 16:00–18:00
🏷️ Labels: Optimization, Graph Theory, Academic Scheduling, Data Science, Networkx
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to explore and optimize various academic and data-related problems using advanced algorithmic approaches and align research papers with institutional specializations.

Key Activities:

  1. Research Alignment: Analyzed the alignment of the paper ‘Debunking Granularity: A Comprehensive Analysis of Aggregate Volatility’ with institutional research specializations, focusing on interdisciplinary connections and departmental interests.
  2. Paper Summary: Provided a structured summary of the paper on aggregate volatility, detailing its main theme, methodology, structure, and institutional relevance.
  3. Optimization Planning: Explored optimization strategies for course organization using graph theory, linear programming, rule-based systems, genetic algorithms, and classification algorithms.
  4. Graph Optimization: Developed strategies for optimizing the modeling of course prerequisites using adjacency matrices and directed graphs, employing topological sorting, shortest paths, quadratic optimization, and clustering techniques.
  5. Level Assignment Optimization: Formulated a formal approach to minimize discrepancies in course level assignments, defining variables, objective functions, constraints, and solution adjustments.
  6. DataFrame Correlation Propagation: Implemented a method to propagate course prerequisites in a Pandas DataFrame using NetworkX and shortest path algorithms, providing a code outline.

Achievements:

  • Successfully aligned research papers with institutional goals.
  • Developed comprehensive optimization frameworks for academic scheduling and course assignments.
  • Implemented a code-based solution for propagating course prerequisites in data structures.

Pending Tasks:

  • Further refine the optimization algorithms for course scheduling.
  • Validate the implementation of the DataFrame correlation propagation method.

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