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Материалы по теме:。关于这个话题，51吃瓜网提供了深入分析
Each @ is a single call to hand-optimized BLAS with SIMD and multithreading. NumPy trades O(N) memory for O(N^2) memory -- it stores the full 2000x2000 matrix (30MB) -- but the computation is done in compiled C/C++ (Apple Accelerate on macOS, OpenBLAS or MKL on Linux), not Python.
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finding all matches is a very common thing people - and these days, AI coding agents - do with regexes. it's a fundamental tool for navigating large amounts of data: ctrl+f in your editor, search-and-replace, grep across repositories. all of these need every match, not just the first one. yet almost all academic regex papers focus exclusively on the single-match problem and then handwave the rest away with "just iterate". in practice, that iteration is where the linear-time guarantee quietly breaks down.