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People who use MATLAB use it for the toolboxes. The language itself is awful.

enriquto 1y ago
> The language itself is awful. As a programming language freak, I must disagree... in what other programming language can you solve a linear system Ax=b in one line x = A\b without any external libraries or imports, just with the base language? I never used any official matlab "toolbox", but still love the language via the octave interpreter. It's so clean and straightforward!
- almostgotcaught 1y ago
  > without any external libraries or imports Why does this matter in the least? Like you must understand that this is a library call right? Like just put `import numpy as np` in your PYTHONSTARTUP and it's the exact same UX in python. https://docs.python.org/3/using/cmdline.html#envvar-PYTHONSTARTUP https://docs.python.org/3/using/cmdline.html#envvar-PYTHONST...
- torrance 1y ago
  Well, in Julia, for one.
  - ok123456 1y ago
    Also APL: b ⌹ A
- anthk 1y ago
  GNU Maxima. Not a general purpose one, but good enough. Also, qalc from libqalculate for trivial stuff.
- _glass 1y ago
  In Python you can use a library, then it is: x = np.linalg.solve(A, b). But yeah, Octave is nice, because it stays very symbolic.
- electroly 1y ago
  In R, this is: x <- solve(A, b)
username223 1y ago
Matlab/Octave is great for numerical programs that perform within an order of magnitude of Fortran. If some things aren't fast enough, you can rewrite them in C or Fortran without too much trouble. If you're doing anything other than numerical computing, it's awful, and you should use a different language. (Source: I did a PhD using a mixture of Octave for numerical stuff, Perl for text-processing and automation, and C++ for the parts that were too slow. Choose the right tool for the job.)
- deleted 1y ago
  [deleted]
- ok123456 1y ago
  Modern Fortran is better all around. The compiler will check usage based on interface. It has a working and supported module system (unlike C++). A couple of openmp pragmas will parallelize it. Multidimensional dense arrays are first class objects. The compiler can emit code with array bounds checking. Keyword and optional arguments. Standardized C FFI. f2py inter-op with Python/numpy. Most people encounter large FORTRAN IV or FORTRAN 77 heirloom codes, and assume that's what Fortran is like in 2025.

> The language itself is awful. As a programming language freak, I must disagree... in what other programming language can you solve a linear system Ax=b in one line x = A\b without any external libraries or imports, just with the base language? I never used any official matlab "toolbox", but still love the language via the octave interpreter. It's so clean and straightforward!

almostgotcaught 1y ago
> without any external libraries or imports Why does this matter in the least? Like you must understand that this is a library call right? Like just put `import numpy as np` in your PYTHONSTARTUP and it's the exact same UX in python. https://docs.python.org/3/using/cmdline.html#envvar-PYTHONSTARTUP https://docs.python.org/3/using/cmdline.html#envvar-PYTHONST...
torrance 1y ago
Well, in Julia, for one.
- ok123456 1y ago
  Also APL: b ⌹ A
anthk 1y ago
GNU Maxima. Not a general purpose one, but good enough. Also, qalc from libqalculate for trivial stuff.
_glass 1y ago
In Python you can use a library, then it is: x = np.linalg.solve(A, b). But yeah, Octave is nice, because it stays very symbolic.
electroly 1y ago
In R, this is: x <- solve(A, b)

username223 1y ago

Matlab/Octave is great for numerical programs that perform within an order of magnitude of Fortran. If some things aren't fast enough, you can rewrite them in C or Fortran without too much trouble. If you're doing anything other than numerical computing, it's awful, and you should use a different language. (Source: I did a PhD using a mixture of Octave for numerical stuff, Perl for text-processing and automation, and C++ for the parts that were too slow. Choose the right tool for the job.)

deleted 1y ago
[deleted]
ok123456 1y ago
Modern Fortran is better all around. The compiler will check usage based on interface. It has a working and supported module system (unlike C++). A couple of openmp pragmas will parallelize it. Multidimensional dense arrays are first class objects. The compiler can emit code with array bounds checking. Keyword and optional arguments. Standardized C FFI. f2py inter-op with Python/numpy. Most people encounter large FORTRAN IV or FORTRAN 77 heirloom codes, and assume that's what Fortran is like in 2025.