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beeb.li | ||
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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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andreabergia.com
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| | | | | This post is part of the Writing a JVM in Rust series. In this post, I will discuss how rjvm parses .class files. The code I will discuss today is contained in the reader crate. A warning before you read: this is the earliest part of the project and, since I have written this project to learn Rust, it is also the one that contains the least idiomatic code. Don't take this as an example of the best Rust ever written! | |
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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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lukesingham.com
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| | | Grokking Algorithms is a beautifully formatted book that explains complex material simply using pictures, analogies and high level practical explanations. This post is a review and summary of the Grokking Algorithms book. | ||
