Unpopular (?) Opinion: Matrices are a great data structure. But, using linear algebra ops for the vectorization speedup alone is often an obfuscating optimization. A well-commented loop over the clearly-named elements is often better. (Plus, ya know -- numba).
Like, I *wouldn't* do it in an ML algorithm (unless, as @kaznatcheev pointed out, the reader has no LA experience). But, I would do it if I'm trying to communicate an unfamiliar, out-of-core statistic that supports some research.
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One of the uses of computational models (over more abstract alternatives) is to communicate with less technical audiences. So this can certainly be an argument for loops in the context of communicative comp. models, or models used for rhetoric or non-technical pedagogy.
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I think I'd argue the opposite. Abstract models are supposed to hide the details, making them more digestible. Focus is shifted from how it's happening to what is happening.
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