So what I'd like to know is how that "quite different" approach to problems differs from the standard mathematician approach?
I've been playing with J lately. I've also been a longtime numpy user, going back to the days when it was still numarray. Maybe I'm just writing numpy in J, but I find that my approach in both languages is more or less identical: set up a vector, do some matrix operations, maybe some statistical aggregates, write down the answer.
Can you provide an example for which the APL approach is significantly different from what one would normally do? It might help me understand what insights I'm supposed to gain.
It's quite similar to the standard mathematician approach. It's extremely different from the standard (imperative-trained) programmer approach.
(Note your use of "write down the answer": this is a giveaway that you understand it so well that you're not even aware of your understanding, and might therefore find it hard to explain)
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So what I'd like to know is how that "quite different" approach to problems differs from the standard mathematician approach?
I've been playing with J lately. I've also been a longtime numpy user, going back to the days when it was still numarray. Maybe I'm just writing numpy in J, but I find that my approach in both languages is more or less identical: set up a vector, do some matrix operations, maybe some statistical aggregates, write down the answer.
Can you provide an example for which the APL approach is significantly different from what one would normally do? It might help me understand what insights I'm supposed to gain.
It's quite similar to the standard mathematician approach. It's extremely different from the standard (imperative-trained) programmer approach.
(Note your use of "write down the answer": this is a giveaway that you understand it so well that you're not even aware of your understanding, and might therefore find it hard to explain)