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 July 3rd, 2017, 05:52 PM #1 Newbie   Joined: Jul 2017 From: US Posts: 12 Thanks: 0 Publishers Hello, I recently fine-tuned a mathematical method that creates representations of the integrals of power functions with a tight degree of accuracy around 1% off of the real value at the point of max deviation. I had three questions that you would hopefully be able to answer for me: 1.) Do you believe that this would be significant enough of a finding to get published? 2.) If yes, where should I submit it to? I unfortunately finished it a year after finishing college, but I started working on it in my freshman year. 3.) Where would I get a detailed review of the work? (I am pretty sure many people would rather not read a 50 page paper on a method that may or may not be significant.) Last edited by skipjack; July 4th, 2017 at 08:23 AM.
 July 4th, 2017, 08:06 PM #2 Senior Member   Joined: Feb 2016 From: Australia Posts: 1,737 Thanks: 606 Math Focus: Yet to find out. A quadrature method for approximating power functions?
July 5th, 2017, 04:22 PM   #3
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 Originally Posted by Joppy A quadrature method for approximating power functions?
Nope, what it is, is what I call a mirror function. A mirror function is a function that behaves exactly like or nearly like the function it is based off of. A simple example of this is:

((x-5)/2)-(sqrt(x^2)/2)+(7/2) which is a mirror of 1.

It is pretty much a simple form of Calculus of Variations. https://en.wikipedia.org/wiki/Calculus_of_variations

July 5th, 2017, 05:56 PM   #4
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 Originally Posted by Tkipp ((x-5)/2)-(sqrt(x^2)/2)+(7/2) which is a mirror of 1.
sqrt(x^2). Is this intended as a joke? Like an obfuscated code contest but for math?

July 5th, 2017, 06:07 PM   #5
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 Originally Posted by Maschke sqrt(x^2). Is this intended as a joke? Like an obfuscated code contest but for math?
I did say simple example didn't I?
I am just showing simple functions can be put together to represent anything from something as simple as a constant such as 1 or as complex as the integral of a power function.

 July 16th, 2017, 03:27 PM #6 Senior Member   Joined: Oct 2009 Posts: 628 Thanks: 190 Do you mean integrals like $\int x^p dx$? Sorry if I fail to understand, but don't we already know exactly what these integrals are? Thanks from Joppy
July 17th, 2017, 04:56 PM   #7
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 Originally Posted by Micrm@ss Do you mean integrals like $\int x^p dx$? Sorry if I fail to understand, but don't we already know exactly what these integrals are?
Yep, except it is for functions like exp(x^2), x^x, etc, which currently do not have such a representation.

Last edited by Tkipp; July 17th, 2017 at 05:00 PM.

July 17th, 2017, 05:01 PM   #8
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 Originally Posted by Tkipp Yep, except for functions like exp(x^2), x^x, etc.
OK nice. It has been rigorously proven that these integrals cannot be found analytically in terms of elementary functions.

However, we do already know how to approximate these integrals to any degree of accuracy. So if you want to publish this, then you'll need to show somehow that your method is better than the currently known ones. Better could mean that it is more numerically stable, or it is faster, or...

If you could show this, then yes, people would be very interested in this result. You don't even have to show it mathematically, doing a simulation study where your technique beats the competition would already be quite good!

 July 17th, 2017, 05:45 PM #9 Newbie   Joined: Jul 2017 From: US Posts: 12 Thanks: 0 Here is a run I took with exp(x^2). The first is the integral values, the second the derivative of my function divided by exp(x^2) minus 1. As you can see it is fairly accurate, and these graphs capture the max error deviation, which is centered around x=1, -1. As the approaches both positive and negative infinity the error ratio goes to zero.
July 17th, 2017, 05:49 PM   #10
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Quote:
 Originally Posted by Tkipp Here is a run I took with exp(x^2). The first is the integral values, the second the derivative of my function divided by exp(x^2) minus 1. As you can see it is fairly accurate, and these graphs capture the max error deviation, which is centered around x=1, -1. As the approaches both positive and negative infinity the error ratio goes to zero.
This is not accurate at all. It should give the same value as the standard methods. You might say the error is small, but in a large program, such small errors will compound quickly.

So if your method doesn't give the correct answer, what is its benefit? Is it faster? More stable?

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