# How bad is my maths? (numerical ODE method)

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• m4r35n357
In summary, the author describes an implementation of a method that may not be well known, and which he hopes will be more easily understood. He provides a summary of the content without providing any responses or replies to questions.
m4r35n357
TL;DR Summary
My mathematics is not rigorous, so how does this read to a real mathematician?
I have written some ODE solvers, using a method which may not be well known to many. This is my attempt to explain my implementation of the method as simply as possible, but I would appreciate review and corrections.

At various points the text mentions Taylor Series recurrences, which I only mention briefly as I am concentrating on describing the integration method. These are documented in detail here.

Bear in mind, this is a working method, it is just my description of it that requires review!

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I have only skimmed this but it seems to me that, rather than follow the method in the paper you linked (which requires calculation of higher order derivatives of the ODE), you are approximating the higher derivatives using differences. How is this different from a Runge-Kutta method?

pbuk said:
How is this different from a Runge-Kutta method?
Entirely different. I do not use differences anywhere. Have you looked at the recurrences paper (third link), because that is a big part of the technique?
My aim is to explain the Jorba-Zou method more simply and thoroughly, because it deserves more attention.

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Oh so you are calculating higher order derivatives of the ODE? I don't think therefore that you can 'only mention briefly' the derivation of the recurrence relation between terms of the Taylor Series because this derivation, which is specific to each system of ODEs, is fundamental to the process.

You say that you want to concentrate on describing the 'integration method' itself, but I can't see where you do that and in any case this isn't this simply the stepwise calculation of a truncated Taylor series?

You say that your aim is to explain the methods described in the Jorba and Zou paper more simply and thoroughly, but there does not seem to be any thoroughness in your explanation. For instance I would expect your description to describe and justify the calculation of step-size and provide bounds for the calculation error and roundoff error propogation.

What do you hope to achieve by using Horner's method? The calculation of the truncated Taylor series sum involves only a handful of operations each step so is not worth optimising - and won't you need to recalculate the coefficients for Horner's method at each step anyway? In any case, you need to consider the impact of roundoff error propogation in this method.

I wouldn't call myself a real mathematician, but I do know what I expect to see in a paper describing a numerical method so I hope this gives you a few pointers.

pbuk said:
Oh so you are calculating higher order derivatives of the ODE? I don't think therefore that you can 'only mention briefly' the derivation of the recurrence relation between terms of the Taylor Series because this derivation, which is specific to each system of ODEs, is fundamental to the process.
I am seeking only a review of my bit, particularly notation. The recurrences are comprehensively documented by an expert in the field (linked in the OP), what can I contribute to that? Addition is also fundamental to the process, but I am not going to explain that!
pbuk said:
You say that you want to concentrate on describing the 'integration method' itself, but I can't see where you do that and in any case this isn't this simply the stepwise calculation of a truncated Taylor series?
Not sure what you mean by "stepwise calculation of a truncated Taylor series". My description is the only thing in the document! I am explaining how the derivative "jets" are calculated iteratively as opposed to numerical differences or symbolic differentiation.
pbuk said:
You say that your aim is to explain the methods described in the Jorba and Zou paper more simply and thoroughly, but there does not seem to be any thoroughness in your explanation. For instance I would expect your description to describe and justify the calculation of step-size and provide bounds for the calculation error and roundoff error propogation.
No, just the basic method.
pbuk said:
What do you hope to achieve by using Horner's method? The calculation of the truncated Taylor series sum involves only a handful of operations each step so is not worth optimising - and won't you need to recalculate the coefficients for Horner's method at each step anyway? In any case, you need to consider the impact of roundoff error propogation in this method.
Horner's method is the integration in this case; it determines the value at the next time step.
pbuk said:
I wouldn't call myself a real mathematician, but I do know what I expect to see in a paper describing a numerical method so I hope this gives you a few pointers.
I am not writing a paper, I just want to document my code. I posted some Lorenz code in an earlier thread. I was asked by @Greg Bernhardt if I wanted to write an insights article on it, but I declined as it was above my pay grade. I saw this as the next best thing.

pbuk said:
You say that you want to concentrate on describing the 'integration method' itself, but I can't see where you do that and in any case this isn't this simply the stepwise calculation of a truncated Taylor series?
Truncated to arbitrary order, to be precise.
pbuk said:
What do you hope to achieve by using Horner's method? The calculation of the truncated Taylor series sum involves only a handful of operations each step so is not worth optimising
Could be in the thousands as above, it is arbitrary (although 8th to 20th order is more typical).

m4r35n357 said:
Could be in the thousands as above
Then I think you should definitely investigate roundoff error propogation more. I see I am not the first to hint at this problem:
Ibix said:
You do seem to get quite different results if you use different approaches to the polynomial (such as pre-calculating the ##h^i## values and summing in order of increasing or decreasing ##i##), but I suppose that's to be expected in a system like this one.

pbuk said:
Then I think you should definitely investigate roundoff error propogation more. I see I am not the first to hint at this problem:
I know about roundoff, and I also know about arbitrary precision arithmetic.
In terms of errors, it is a taylor series, so clearly the method is "like" RK4, but with less truncation error (for order higher than 4), and no finite difference errors.
I'm trying to get someone to actually read (not skim) my stuff and point out actual mistakes in my notation, I'm not looking for homework!
Remember this technique is nearly a century old if you trace the references.

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m4r35n357 said:
I'm trying to get someone to actually read (not skim) my stuff and point out actual mistakes in my notation
This is the official statement: I'm afraid we are not able to discuss personal work as we are no reviewers of papers.

Inofficial, I like to add a few remarks. Your ODE solver requires a login. This is a hurdle not many will be willing to take; me, for example. Then you provide a paper from Jorba and Zou from 2004 whose copyright situation is not clear, and which is possibly not peer reviewed. At least I cannot tell from the link you gave. This means, that it is hard to tell which value this paper has, i.e. whether it contains flaws or not. But let us assume that they presented a valid algorithm for the moment. In order to understand what you might have done, one has to read this paper, which is factually almost another peer review. You cannot expect anyone to do this. However, we assumed that the paper is correct. Then you are searching for someone who is familiar with its content. Something yourself said is unlikely:
m4r35n357 said:
My aim is to explain the Jorba-Zou method more simply and thoroughly, because it deserves more attention.
At least I have read your comment about its attention in this way.

Anyway, I skimmed your work. And here I am indeed experienced in reading papers about algorithms. It doesn't need a closer look to see that it lacks essential parts. The algorithm covers - benevolently counted - 16 lines. The rest are statements and examples, which are not important. So neither is your algorithm documented in a serious way, nor did you a) prove that it works, b) added an analysis of space and time, or c) analyzed the accuracy of your procedure. You didn't even prove it comes to a hold! Those are essential parts of a documentation of an algorithm.

To summarize the above: There are too many hurdles and severe gaps in preparation and presentation.

I'm sorry that this doesn't match your expectations as I see that you put quite some effort in it. The most important recommendation I would give is: Concentrate on the algorithm and its needs and forget examples, links and similar. Those should only build the frame. The ratio 780 : 4900 (section 2 : entire document) is not what one would expect from a documentation.

This thread is closed.

pbuk

## 1. How do I know if my maths is bad?

The best way to determine if your maths skills need improvement is to take a practice test or quiz in the specific area you are concerned about, such as numerical ODE methods. This will give you a clear understanding of your current level of understanding and where you may need to focus your efforts.

## 2. What is a numerical ODE method?

Numerical ODE (ordinary differential equation) methods are mathematical algorithms used to approximate the solutions of differential equations. These methods involve breaking down the problem into smaller, simpler steps and using numerical techniques to solve them.

## 3. Can I improve my maths skills in numerical ODE methods?

Yes, like any other skill, maths skills can be improved with practice and dedication. There are many resources available, such as textbooks, online tutorials, and practice problems, that can help you improve your understanding and proficiency in numerical ODE methods.

## 4. How important is numerical ODE method in scientific research?

Numerical ODE methods are essential in many areas of scientific research, including physics, engineering, and biology. They are used to model and understand complex systems and phenomena, making them a crucial tool for scientists in various fields.

## 5. What are some common mistakes to avoid in numerical ODE methods?

Some common mistakes to avoid in numerical ODE methods include using incorrect formulas or algorithms, not properly setting up initial conditions, and not checking for accuracy and convergence. It is also important to understand the limitations of the method being used and to regularly check for errors in the calculations.

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