Asymptotology has been defined as “the art of dealing with applied mathematical systems in limiting cases”[1] as well as “the science about the synthesis of simplicity and exactness by means of localization".[2]
The field of asymptotics is normally first encountered in school geometry with the introduction of the asymptote, a line to which a curve tends at infinity. The word Ασύμπτωτος (asymptotos) in Greek means non-coincident and puts strong emphasis on the point that approximation does not turn into coincidence. It is a salient feature of asymptotics, but this property alone does not entirely cover the idea of asymptotics and, etymologically, the term seems to be quite insufficient.
In physics and other fields of science, one frequently comes across problems of an asymptotic nature, such as damping, orbiting, stabilization of a perturbed motion, etc. Their solutions lend themselves to asymptotic analysis (perturbation theory), which is widely used in modern applied mathematics, mechanics and physics. But asymptotic methods put a claim on being more than a part of classical mathematics. K. Friedrichs said: “Asymptotic description is not only a convenient tool in the mathematical analysis of nature, it has some more fundamental significance”. M. Kruskal introduced the special term asymptotology, defined above, and called for a formalization of the accumulated experience to convert the art of asymptotology to a science. A general term is capable of possessing significant heuristic value. In his essay "The Future of Mathematics",[3] H. Poincaré wrote the following.
In addition, “the success of ‘cybernetics’, ‘attractors’ and ‘catastrophe theory’ illustrates the fruitfulness of word creation as scientific research”.[4]
Almost every physical theory, formulated in the most general manner, is rather difficult from a mathematical point of view. Therefore, both at the genesis of the theory and its further development, the simplest limiting cases, which allow analytical solutions, are of particular importance. In those limits, the number of equations usually decreases, their order reduces, nonlinear equations can be replaced by linear ones, the initial system becomes averaged in a certain sense, and so on.
All these idealizations, different as they may seem, increase the degree of symmetry of the mathematical model of the phenomenon under consideration.
In essence, the asymptotic approach to a complex problem consists in treating the insufficiently symmetrical governing system as close to a certain symmetrical one as possible.
In attempting to obtain a better approximation of the exact solution to the given problem, it is crucial that the determination of corrective solutions, which depart from the limit case, be much simpler than directly investigating the governing system. At first sight, the possibilities of such an approach seem restricted to varying the parameters determining the system only within a narrow range. However, experience in the investigation of different physical problems shows that if the system's parameters have changed sufficiently and the system has deviated far from the symmetrical limit case, another limit system, often with less obvious symmetries can be found, to which an asymptotic analysis is also applicable. This allows one to describe the system's behavior on the basis of a small number of limit cases over the whole range of parameter variations. Such an approach corresponds to the maximum level of intuition, promotes further insights, and eventually leads to the formulation of new physical concepts.
It is also important that asymptotic analysis helps to establish the connection between different physical theories.The aim of the asymptotic approach is to simplify the object. This simplification is attained by decreasing the vicinity of the singularity under consideration. It is typical that the accuracy of asymptotic expansions grows with localization. Exactness and simplicity are commonly regarded as mutually exclusive notions. When tending to simplicity, we sacrifice exactness, and trying to achieve exactness, we expect no simplicity. Under localization, however, the antipodes converge; the contradiction is resolved in a synthesis called asymptotics. In other words, simplicity and exactness are coupled by an “uncertainty principle” relation while the domain size serves as a small parameter – a measure of uncertainty.
Let us illustrate the “asymptotic uncertainty principle”. Take the expansion of the function
f(x)
{\phin(x)}
f(x)=
| infty | |
| \sum | |
| n=0 |
an\phin(x)
x
0
A partial sum of the series is designated by
SN(x)
N
\DeltaN(x)=|f(x)-SN(x)|
N
x
Based on known properties of the asymptotic expansion, we consider the pair wise interrelation of values
x
N
\Delta
x
N
\Delta
We illustrate these regularities using a simple example. Consider the exponential integral function:
\operatorname{Ei}(y)=
| y | |
| \int | |
| -infty |
e\zeta\zeta-1d{\zeta},y<0
Integrating by parts, we obtain the following asymptotic expansion
\operatorname{Ei}(y)\simey
| infty | |
| \sum | |
| n=1 |
(n-1)!y-n, y
-infty
Put
f(x)=-e-y\operatorname{Ei}(y)
y=-x-1
\DeltaN(x)
f(x)
x
x
f(x)
\Delta1
\Delta2
\Delta3
\Delta4
\Delta5
\Delta6
\Delta7
Thus, at a given
x
N
N
x
Finally, is it worth using asymptotic analysis if computers and numerical methods have reached such an advanced state? As D. G. Crighton has mentioned,[5]