Comment by jiggawatts

I prefer "middle of the road" languages that are high-level AND readable AND have decent performance optimisation for bulk operations. Python with C libraries suffices for a lot of people, Julia similarly is getting popular.

Even the older Mathematica language blows both K and C out of the water for readability and performance:

    m = Table[RandomReal[], 100, 100];
    t = RepeatedTiming[MatrixPower[m, 2]];
    First[t]*1000*1000

    18.0245 

You would have to know literally zero about Mathematica's Wolfram Language to be able to read that clearly! From a standing start you could understand what another person has created. For K, you'd have to have memorized the K-specific syntax. For C, if you hadn't seen the standard patterns for matrix multiplication you'd have to read the code carefully. A lot of it is just noise, like the verbose for loop syntax.

Oh, and Mathematica's matrix power function is:

- Parallel! If I use a 10K x 10K matrix as an input, it uses about 75% CPU on my 8-core laptop. It can complete a single multiplication in 5.3 seconds. For laughs, try that with either K or C and see what you get...

- Extends to negative or fraction powers.

- Has optimisations for applying the matrix power directly to a vector.

- Is extensively documented, unlike the terse K snippet or the hand-rolled C code: https://reference.wolfram.com/language/ref/MatrixPower.html....

Essentially what I'm trying to say is that terseness is an anti-pattern, and doesn't even begin to approach the utility of a well designed high-level language intended for teams of collaborating humans.