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More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
your objection might be to the author of the notation, not the notation itself. or maybe to the review system that's supposed to catch this kind of imprecision. as with any language, it's possible to write nonsense or even self-conflicting statements in mathematical notation.
All in all, imprecise probably was the wrong term. The statements are precise for the people working in this specific "bubble" of research, because they might know the implicit assumptions made and in the worst case, the only one who knows the implicit assumptions is the author.
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
This would be insane to do in code, why is it normal in mathematics?
Note: I had this example in my head while writing https://www.traffic-simulation.de/info/IDMsstar.png
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
For most people, with their limited writing skills, understanding and precision have a tradeoff, and there is no perfect paper.
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
I also sometimes have to translate mathematical notation to algorithms. As a non-mathematician, math notation seems similar to naming all your variables 'a', 'b', 'c' etc. and then writing a key elsewhere that explains that 'b' stands for 'car deceleration rate'. This would be insane to do in code, why is it normal in mathematics? Note: I had this example in my head while writing https://www.traffic-simulation.de/inf…
For programming, the same constraint does not apply.
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
The anti-dote to your problem is to try to write a serious mathematical paper yourself. After spending 6 months writing it, and how many months/years of research, you will realize that writing a paper whose technical results are easy to reproduce and has precise notation, but which also conveys understanding to other human beings is an extremely difficult task, that even the smartest people on the planet can't always…
What's not in the paper but I find quite annoying in scientific articles is excessively loaded notation. Some variables get to have like 4 or 5 subscripts and equations become unreadable. I really appreciate when papers get to say at some point "for the sake of clarity, let drop this or that notation" that are not that relevant to the key ideas. Usually it's in the good quality papers.
Earlier quoted context omitted.
I also sometimes have to translate mathematical notation to algorithms. As a non-mathematician, math notation seems similar to naming all your variables 'a', 'b', 'c' etc. and then writing a key elsewhere that explains that 'b' stands for 'car deceleration rate'. This would be insane to do in code, why is it normal in mathematics? Note: I had this example in my head while writing https://www.traffic-simulation.de/inf…
Because one of the main purposes of mathematical symbols is to be manipulated by humans by hand, so short variable names are essential. For programming, the same constraint does not apply.
Are mathematicians ever frustrated that they don't understand what the variables mean? Or are you able to look at the above equation and infer the meaning of the variables based on experience?
I understand that there are symbols such as "delta" which basically always mean the same thing. But would you have been able to tell what "b" meant without someone telling you?
More of a rant, but since I began to implement algorithms from various papers for my PhD, my aversion to mathematical notation grows steadily. More often than not it's imprecise and hard to reproduce.
