What software for optimization surface of 4 parameters?

AI Thread Summary
For optimizing a function with four parameters and ensuring a stable maximum, R is recommended over Matlab, especially with a dataset of 60,000 values. The Nelder-Mead optimization method is suggested, as it does not require an explicit function form, only function values. Users can find more resources on the Nelder-Mead algorithm, often referred to as the "simplex" method, in various optimization literature. Richard Brent's book on algorithms for minimization without derivatives is also recommended for further reading. Overall, R and the Nelder-Mead method appear to be suitable choices for this optimization task.
Tosh5457
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Hello,

I have data for optimization that contains different values of 4 different parameters, and the respective function value based on them. I want to find the maximum of this function, while making sure it's a "stable" maximum, i.e., for nearby parameters, the value of the function shouldn't change that much. My only question is what tool to use for this, since I have 60k values in total? I have experience in Matlab, can that be done in a reasonable amount of time there? I also have some basic knowledge of R, and can learn fast anyway, is this more suitable to do in R?

Thanks!
 
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Tosh5457 said:
Hello,

I have data for optimization that contains different values of 4 different parameters, and the respective function value based on them. I want to find the maximum of this function, while making sure it's a "stable" maximum, i.e., for nearby parameters, the value of the function shouldn't change that much. My only question is what tool to use for this, since I have 60k values in total? I have experience in Matlab, can that be done in a reasonable amount of time there? I also have some basic knowledge of R, and can learn fast anyway, is this more suitable to do in R?

Thanks!

I would definitely go with R, even (or I should say especially) with 60,000 values. Also, but depending on the context, I think the Nelder-Mead optimization method would be an interesting approach to try.

I've used the neldermead package before and it served me well.

More information here : https://cran.r-project.org/web/packages/neldermead/neldermead.pdf
 
h6ss said:
I would definitely go with R, even (or I should say especially) with 60,000 values. Also, but depending on the context, I think the Nelder-Mead optimization method would be an interesting approach to try.

I've used the neldermead package before and it served me well.

More information here : https://cran.r-project.org/web/packages/neldermead/neldermead.pdf


Thank you, will definitely look into that method in R.
 
Ok, Nelder-mead won't work, because I don't have the function explicitly...
 
Tosh5457 said:
Ok, Nelder-mead won't work, because I don't have the function explicitly...
The explicit form of the function is not necessary. The Nelder-Mead algorithm only requires function values.

The algorithm is often called the "simplex" method, maybe you can find more information about it under that label. Also, many books cover the subject, e.g. Richard Brent's book Algorithms for Minimization Without Derivatives.
 
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