[Numerical analysis] Stability and condition of Newton's method

[srn]

I am confused by the concept of stability and condition. As I understand it, condition is defined by how much the output changes when the input changes. But why is it linked to the problem and not the algorithm? What if I have two algorithms that calculate the same thing but in a completely different manner, and the same change in input causes algorithm (a) to return more erroneous results than algorithm (b), can I not conclude (a) is worse conditioned than (b)? Or is that what stability is about? From Wikipedia: the difference between function and approximations, or "whether or not errors blow up". Is condition simply [itex]f(x) - f(x+\epsilon)[/itex] and stability [itex]f_{theory}(x) - f(x)[/itex]? If so I more or less understand it, but I can't apply it to Newton's method, for example.

Consider the fractal in http://mathworld.wolfram.com/NewtonsMethod.html. It is obvious that a small change in starting value can cause it to converge to a completely different root. Is the method then ill-conditioned? But condition is linked to the problem, so it would be the same for a different root solving method; but i.e. Müller's method has less trouble with changing start values. So it's dependent on algoritm and hence not condition. But then what does the fractal tell you?

Is it stability? I don't think so because there's 'nothing wrong', I mean, it's still going t oa root, it's not diverging or anything. I understand substracting two nearly equal numbers is unstable because the error "blows up", but in this case? Speaking about that, 1) how would you quantify stability when you have no theoretical values? Would you use a function built into a computerprogram and assume that it is implemented as accurately as possible and then compare with that? 2) how come you even get differences in accuracy between methods? In this case you would iteratively calculate a root until the difference between consequetive terms is lower than a tolerance. If you use the same tolerance, how can two values differ? It would mean that going from the step where error > tol to the step where it is <= tol, one method "added" more than the other.

 

[chiro]

Hey srn.

These ideas might give you an idea:

http://en.wikipedia.org/wiki/Butterfly_effect

 

[boit]

I like Newton's method. It prevents the absurdity of having a square root of number being equal in magnitude to the square without breaking a sweat. That way, I'm comfortable in having this neat equations
sqrt. 1=0.9r
and conversely
sqrt. 0.9r=1
(I noticed that square roots of fractions have a higher numerical value than their squares while square roots of whole numbers are lesser).
NB. Am not insinuating that .9r is any lesser than 1. I've seen the FAQs.

 

[HallsofIvy]

I presume that by "fraction" you mean "a number between 0 and 1". Mathematically, a fraction is any number of the for a/b where a and b are integers. That is, 30/5 is a fraction and 0.5 is not (unless you use the bastard phrase "decimal fraction").

Yes, if 0< x< 1 then, multiplying each part by the postive number x, 0< x^2< x while if 1< x, then x< x^2.

 

[CellCoree]

I can provide some clarification on the concepts of stability and condition in numerical analysis, specifically in the context of Newton's method.

Stability refers to the ability of an algorithm to produce accurate and consistent results, even when small changes are made to the initial input. In other words, a stable algorithm should not produce drastically different results for slightly different inputs. In the case of Newton's method, the fractal you mention is an example of instability, as small changes in the starting value can lead to completely different roots being found. This is not desirable, as it means the algorithm is not producing reliable results.

On the other hand, condition refers to the sensitivity of the output to changes in the input. In the context of numerical analysis, this is often measured by the condition number, which can give an indication of how much the output will change when the input is perturbed. In the case of Newton's method, the condition number can be affected by the specific problem being solved and the starting value chosen. A problem with a high condition number can make the algorithm less reliable, as small changes in the input can lead to large changes in the output.

To address your confusion about the link between condition and the problem versus the algorithm, it is important to note that the condition number is specific to a particular problem and input, while the algorithm is the method used to solve that problem. So, while different algorithms may produce different results for the same problem, the condition number remains the same. This means that a problem can be well-conditioned (low condition number) for one algorithm, but poorly conditioned (high condition number) for another.

To answer your questions: 1) Quantifying stability without theoretical values can be challenging, but it can be done by comparing the results of the algorithm to known solutions or using benchmark problems with known solutions. 2) Differences in accuracy between methods can arise due to the specific implementation of the algorithm, the tolerance used, and the specific problem being solved. Even with the same tolerance, small differences in the calculations can lead to different outputs, resulting in slightly different results.

In summary, stability and condition are both important considerations in numerical analysis, and they are related but distinct concepts. While stability refers to the reliability and consistency of an algorithm, condition measures the sensitivity of the output to changes in the input. In the case of Newton's method, both stability and condition can be affected by the specific problem being solved and the starting value chosen.