What fields of maths are most beneficial to LabVIEW programmers?

[TheStrangeQuark]

Hello,

I'm not sure if this is the right forum for a question like this. I'm pretty new to LabVIEW and have a background in mathematics and computer science. I have a lot of interest in LabVIEW and want to become better at it. I have been trying to figure out what fields of maths would be beneficial to study that would help with LabVIEW programming. I was thinking graph theory since a lot of the LabVIEW interface is making graphs, so having an understanding of graph theory may help with efficiently making large programs or with algorithms. I'm curious if any of you have studied certain fields of maths and found them to be applicable to your LabVIEW programming.

[shoneill]

I've found adding, subtracting, multiplication and division to be quite useful......

But on a more serious note.  I'm sure there are areas of mathematics I don't even know exist which may or may not be helpful.  But if you want to go into pure mathematics as opposed to applied mathematics, your chances are greatly different to be able to apply when doing productive work.

I studied statistics, calculus and so on at University.  I've rarely needed to understand more than the basics.  Trigonometry helped a bit due to the fact that we use a lot of modulators and demodulators but the effect was minimal.  Also the numerical theory behind filters (Kalman, Butterworth and so on) can be very useful.

Beyond this, I'm simply not qualified to answer.

[infinitenothing]

Linear algebra is very useful for control theory, simulation, and machine learning.

I posted what I thought was a math problem a while back. I wasn't really sure what the domain was though. 

[ensegre]

I would say: the fields which are more beneficial are those which are useful to the problem you have to deal with. You do signals, functional analysis is good. You do computational geometry, geometry is good. You do image processing... you name it. Labview is only a programming tool. It's not that you become more proficient in Labview because you know a special branch of maths, such as, you know graph theory, you are good to grasp diagrams. [in fact LV diagrams are just a representation of dataflow, more kin to an electronics diagram than to formal graph theory].

Rather, on general terms, I would say numerical analysis and sound principles of algorithm design really help. How to make an efficient algorithm for doing X, how truncation errors propagate, how to optimize resource use, etc. But this is true of any programming language used for practical problem solving. Formal language theory, compilers - not really, LV conceals these details from you. Unless your task is to implement a compiler in G...

[MarkCG]

probably not graph theory. I highly, highly, highly recommend recommend DSP for scientists and engineers by Steven W. Smith

 http://www.dspguide.com/pdfbook.htm

if you are working with LabVIEW there is probably something in there relevant to what you are doing.

[Jordan Kuehn]

8 hours ago, MarkCG said:

probably not graph theory. I highly, highly, highly recommend recommend DSP for scientists and engineers by Steven W. Smith

 http://www.dspguide.com/pdfbook.htm

if you are working with LabVIEW there is probably something in there relevant to what you are doing.

Definitely DSP. I don't know the book MarkCG recommended, but understanding the fundamentals of sampling and signal processing is at the heart of most of the use cases of LabVIEW.  Now you can get by with the tools NI has developed for you, but you sound like you are interested in a theoretical knowledge and rightly so! If you can understand the math in a course like this, you've already got most of the more fundamental math disciplines under your belt.

[TheStrangeQuark]

11 hours ago, MarkCG said:

probably not graph theory. I highly, highly, highly recommend recommend DSP for scientists and engineers by Steven W. Smith

 http://www.dspguide.com/pdfbook.htm

if you are working with LabVIEW there is probably something in there relevant to what you are doing.

I read this book back when I was doing some signal analysis with Python a couple years ago. It's a great book, I might need to go back through it again.