2.20 Post-Reading Diary:

Article: The Traveling Salesman Problem – The Princeton Companion

The Traveling Salesman Problem (TSP) explores finding the shortest possible route that visits each city once and returns to the start. It's NP-hard, meaning that as the number of cities grows, the number of possible routes grows exponentially, making it computationally difficult to solve for large datasets.

Key takeaways:

  • Computational Complexity: TSP is inherently hard to solve due to its NP-hardness.
  • Algorithms: Exact algorithms provide optimal solutions but are inefficient for large cases, while approximation algorithms offer near-optimal solutions in less time.
  • Applications: TSP has practical uses in logistics, circuit design, and even DNA sequencing.

Reflection: I’m fascinated by how a simple problem can lead to complex challenges in computation. It also highlighted the importance of approximation methods for solving real-world problems when exact solutions aren’t feasible.

Next steps: I’d like to explore the heuristics used to approximate solutions for TSP and the ongoing research in this area.

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2.20 Post-Reading Diary: