Stewart Cheifet, creator of The Computer Chronicles, dead at 87
33 by spankibalt | 17 comments on Hacker News.
Wednesday, December 31, 2025
Tuesday, December 30, 2025
Monday, December 29, 2025
New top story on Hacker News: Show HN: Per-instance TSP Solver with No Pre-training (1.66% gap on d1291)
Show HN: Per-instance TSP Solver with No Pre-training (1.66% gap on d1291)
4 by jivaprime | 0 comments on Hacker News.
OP here. Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems. I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100. The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope. Instead of pre-training, I designed an inductive bias based on the topological/geometric structure of these exception edges. The agent receives guides on which edges are likely promising based on micro/macro structures, and PPO fills in the gaps through trial and error. It is interesting to see RL reach this level without a dataset. I have open-sourced the code and a Colab notebook for anyone who wants to verify the results or tinker with the 'exception edge' hypothesis. Code & Colab: https://ift.tt/dx93voO Happy to answer any questions about the geometric priors or the PPO implementation!
4 by jivaprime | 0 comments on Hacker News.
OP here. Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems. I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100. The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope. Instead of pre-training, I designed an inductive bias based on the topological/geometric structure of these exception edges. The agent receives guides on which edges are likely promising based on micro/macro structures, and PPO fills in the gaps through trial and error. It is interesting to see RL reach this level without a dataset. I have open-sourced the code and a Colab notebook for anyone who wants to verify the results or tinker with the 'exception edge' hypothesis. Code & Colab: https://ift.tt/dx93voO Happy to answer any questions about the geometric priors or the PPO implementation!
Sunday, December 28, 2025
New top story on Hacker News: Ask HN: Best Podcasts of 2025?
Ask HN: Best Podcasts of 2025?
22 by adriancooney | 14 comments on Hacker News.
The Rest is Politics, Leading, Philosophize This and Stratechery (paid) are the podcasts that stood out the most in 2025. Curious what other HNers listen to.
22 by adriancooney | 14 comments on Hacker News.
The Rest is Politics, Leading, Philosophize This and Stratechery (paid) are the podcasts that stood out the most in 2025. Curious what other HNers listen to.
Saturday, December 27, 2025
Friday, December 26, 2025
Thursday, December 25, 2025
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