Minimizing by trisecting a function dependent on 3 parameters

In summary: I guess c1 is some kind of scale factor so that |yk| is near 1 (so that the argument of exp is not too large), while the values a and θ are the same for all k and are the same for all runs.So the first function is for calculating the value of f(v) for a given set of parameters, and the second function is for deciding which range of parameters to keep and which to discard.In summary, the author suggests using a trisection method to guess the nonlinear parameter in a function with three unknown parameters. This involves using the "robust linear estimation" method for each of the four values found through trisection and discarding the least promising range of parameters. The first
  • #1
borson
30
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Hi and thanks to everyone for his/her attention. I have to minimize a function that depends on several parameters. The aim of minimizing that function is to actually guess these parameters, which are unknown. The thing is that the author of the pdf from which I have to make the calculations, does not specify very well how to carry it out. There are 3 parameters the function depends on (one of them nonlinear), and the author says that first off we have to figure out the nonlinear parameter, by trisecting a determined interval, and afterwards guess the spare ones by means of another function that will also have to be minimized. So here is where I am confused. How am I supossed to minimize a function by trisection paying attention to just one parameter? I mean, what do I have to do with the other ones? How should I do it?

Here is the part of the pdf in which that function is shown:

you can find the whole pdf here:http://www.roulettephysics.com/wp-content/uploads/2014/01/Roulette_Physik.pdf

that function is in the 13 page.

thank you all for your attention and I hope you can help me :)
 
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  • #2
borson said:
Hi and thanks to everyone for his/her attention. I have to minimize a function that depends on several parameters. The aim of minimizing that function is to actually guess these parameters, which are unknown. The thing is that the author of the pdf from which I have to make the calculations, does not specify very well how to carry it out. There are 3 parameters the function depends on (one of them nonlinear), and the author says that first off we have to figure out the nonlinear parameter, by trisecting a determined interval, and afterwards guess the spare ones by means of another function that will also have to be minimized. So here is where I am confused. How am I supossed to minimize a function by trisection paying attention to just one parameter? I mean, what do I have to do with the other ones? How should I do it?

Here is the part of the pdf in which that function is shown:

you can find the whole pdf here:http://www.roulettephysics.com/wp-content/uploads/2014/01/Roulette_Physik.pdf

that function is in the 13 page.

thank you all for your attention and I hope you can help me :)
Not sure, but I think the idea is to iterate as follows:
  1. set the range [φmin, φmax] for φ as [0, 2π]
  2. trisect the range, i.e. consider values φmin, 2φmin/3+ φmax/3, φmin/3+2φmax/3, φmax for φ
  3. for each of the four values, v1 to v4, use "robust linear estimation" (whatever that is) to find the corresponding η and Ω2f;
  4. discard the first or last third of the range for φ which appears not to be promising; this assumes the behaviour is not too jerky
  5. repeat from 2.
The discarding rule, I guess, would be
if f(v2)<f(v3) discard [v3, v4], else discard [v1, v2].
 
  • #3
haruspex said:
Not sure, but I think the idea is to iterate as follows:
  1. set the range [φmin, φmax] for φ as [0, 2π]
  2. trisect the range, i.e. consider values φmin, 2φmin/3+ φmax/3, φmin/3+2φmax/3, φmax for φ
  3. for each of the four values, v1 to v4, use "robust linear estimation" (whatever that is) to find the corresponding η and Ω2f;
  4. discard the first or last third of the range for φ which appears not to be promising; this assumes the behaviour is not too jerky
  5. repeat from 2.
The discarding rule, I guess, would be
if f(v2)<f(v3) discard [v3, v4], else discard [v1, v2].

It seems a good idea.
I will try to do that, though I do not understand the last function either. I do not have any idea of what the yk and the xk stand for.
Also, considering the discarding rule, regarding what you have said, it seems that I only need the second function for discarding. So then, why is the first function for?
At the beginning I am obliged to use the f(v) of the second function (as I do not have any guess of the linear parameters to use in the first one yet), to see which set of values for the parameters yield the most minimum value, but once I do that and by means of that I obtain values for that parameters, should I keep using the second function for discardingn or the first one?
Thanks for replying :)
 
  • #4
borson said:
I do not have any idea of what the yk and the xk stand for.
These just stand for terms in the equation at (42) which do not depend on η or Ω. So yk=c1e-2aθfk and xk represents everything inside the square brackets.
 

FAQ: Minimizing by trisecting a function dependent on 3 parameters

How do you define "minimizing by trisecting"?

"Minimizing by trisecting" refers to the process of finding the minimum value of a function that depends on three parameters by dividing the parameter space into three equal parts and evaluating the function at the boundaries of each part.

What is the purpose of minimizing by trisecting a function dependent on 3 parameters?

The purpose of minimizing by trisecting is to find the combination of parameters that results in the lowest possible value of the function. This is useful in various fields of science and engineering, such as optimization problems in mathematics and parameter estimation in statistics.

How is minimizing by trisecting different from other optimization techniques?

Minimizing by trisecting is a specific method of optimization that is most suitable for functions with three parameters. It differs from other techniques, such as gradient descent or genetic algorithms, which are more general and can be applied to functions with any number of parameters.

What are the advantages of using the trisecting method for minimizing a function?

One advantage of using the trisecting method is that it is relatively easy to implement and does not require complex mathematical calculations. Additionally, it can be more efficient than other methods for certain types of functions, especially those with a single global minimum.

Are there any limitations to minimizing by trisecting a function dependent on 3 parameters?

Yes, there are some limitations to this method. One limitation is that it may not be suitable for functions with multiple local minima, as it may not accurately identify the global minimum. Additionally, it may not be applicable to functions with a large number of parameters, as the number of evaluations required to find the minimum may become too high.

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