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matbesancon.xyz | ||
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bayesianneuron.com
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| | | | | [AI summary] The user has shared a detailed exploration of optimizing the 0/1 Knapsack problem using dynamic programming with Python and NumPy. They discuss various optimization techniques, including reducing memory usage with a 2-row approach, vectorization using NumPy's `np.where` for faster computation, and the performance improvements achieved. The final implementation shows significant speedups, especially for large-scale problems, and the user highlights the importance of vectorization and efficient memory management in computational tasks. | |
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www.msoos.org
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| | | | | [AI summary] The article explains the various complexities and approaches to SAT and SMT model counting, including satisfiability checking, approximate and exact counting, model enumeration, and optimization. | |
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jaketae.github.io
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| | | | | Update: The code was modified with further optimizations. In particular, instead of checking the trie per every DFS call, we update the trie pointer along the DFS call so that the trie does not have to be queried repeatedly. | |
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gist.github.com
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| | | GitHub Gist: instantly share code, notes, and snippets. | ||
