Perturbation theory | Wikipedia audio article
Channel: wikipedia tts
Date: 25/12/2018

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Perturbation_theory


00:02:25 1 General description
00:05:47 2 Examples
00:07:10 3 History
00:08:24 3.1 Beginnings in the study of planetary motion
00:09:56 4 Perturbation orders
00:10:26 5 In chemistry
00:11:22 6 See also



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SUMMARY
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Perturbation theory comprises mathematical methods for finding an approximate solution to a problem, by starting from the exact solution of a related, simpler problem. A critical feature of the technique is a middle step that breaks the problem into "solvable" and "perturbation" parts. Perturbation theory is applicable if the problem at hand cannot be solved exactly, but can be formulated by adding a "small" term to the mathematical description of the exactly solvable problem.
Perturbation theory leads to an expression for the desired solution in terms of a formal power series in some "small" parameter – known as a perturbation series – that quantifies the deviation from the exactly solvable problem. The leading term in this power series is the solution of the exactly solvable problem, while further terms describe the deviation in the solution, due to the deviation from the initial problem. Formally, we have for the approximation to the full solution A, a series in the small parameter (here called ε), like the following:

  
    
      
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    {\displaystyle A=A_{0}+\varepsilon ^{1}A_{1}+\varepsilon ^{2}A_{2}+\cdots }
  In this example, A0 would be the known solution to the exactly solvable initial problem and A1, A2, ... represent the higher-order terms which may be found iteratively by some systematic procedure. For small ε these higher-order terms in the series become successively smaller.
An approximate "perturbation solution" is obtained by truncating the series, usually by keeping only the first two terms, the initial solution and the "first-order" perturbation correction

  
    
      
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    {\displaystyle A\approx A_{0}+\varepsilon A_{1}~.}