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beeb.li | ||
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0x80.pl
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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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cprimozic.net
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| | | | | A detailed summary of the techniques I used to optimize my Advent of Code 2024 solution for Day 9 Part 2. Employs a variety of techniques including algorithmic shortcuts, bespoke data structures, and low-level optimizations + SIMD. | |
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reorchestrate.com
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