Steepest descent approximation

In summary, the conversation discusses the method of steepest descent in the context of quantum field theory. The main question is how to compute the integral in the limit of very small h, with the explanation that the corrections are of the form e^{-O(h ^{1/2})}. The conversation also mentions the use of the incomplete Airy function and the possibility of the f'' term heading towards zero.
  • #1
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Hi all, I am reading now Zee's book "Quantum Field Theory in a Nutshell", there in Apendix 2 of Chapter I.2 the method of steepest descent is briefly described. The part where I have a question is almost self contained and half a page long, so I attached the screen shot of it (formula 19). Anyway, the question is the following:
The only prior information required is the Gaussian integral [tex]\int_{-\infty}^\infty dx e^{-1/2ax^ 2} =(\frac {2\pi}{a})^ {1/2}[/tex]
To compute the integral [tex]I=\int_{-\infty}^\infty dq e^{-1/h f(q)}[/tex] in the limit of very small h, we say that the integral is dominated by the minimum of f, and expanding f near that point up to quadratic terms, [tex]f(q)=f(a)+1/2f''(a)(q-a)^{2}+O[(q-a)^{3}][/tex] we obtain
[tex]I = e^{-(1/h)f(a)} (\frac {2\pi h}{f''(a)})^{1/2} e^{-O(h ^{1/2})}[/tex]
So the first part of the right hand side is ok, it's just the Gaussian integral, but how he knows that the corrections are of the form [tex]e^{-O(h ^{1/2})}[/tex]?? Please, at least tell the general idea how it can be shown. Simply keeping also the terms of the next (third) order, i.e. (q-a)^3, gives a very hard integral, at least for me, to evaluate.
 

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  • #2
It does not seem obvious : see for exemple,

methode_de_laplace by Noé Cuneo

where the demonstration is very strict !
 
  • #3
Keeping the third order terms results in the incomplete Airy function as a solution. This would be used to compute approximations where the quadratic term doesn't dominate. When this happens the f'' may be heading for zero. This is not the origin of the O(1/h^1/2) term. That comes from doing an expansion in powers of 1/h.
 

Related to Steepest descent approximation

1. What is steepest descent approximation?

Steepest descent approximation is a numerical method used to find the minimum or maximum of a function. It is also known as the steepest descent method or gradient descent method.

2. How does steepest descent approximation work?

In steepest descent approximation, the algorithm starts at an initial point and moves in the direction of the negative gradient of the function at that point. This process is repeated until a local minimum or maximum is reached.

3. What are the advantages of using steepest descent approximation?

Steepest descent approximation is a simple and efficient method for finding the minimum or maximum of a function. It can be applied to a wide range of functions and is relatively easy to implement.

4. Are there any limitations to steepest descent approximation?

One limitation of steepest descent approximation is that it may converge slowly or get stuck in a local minimum or maximum. It also requires the function to be differentiable, which may not always be the case.

5. How is steepest descent approximation different from other optimization methods?

Steepest descent approximation is a first-order optimization method, meaning it only uses information from the first derivative of the function. Other methods, such as Newton's method, use information from higher-order derivatives, making them more accurate but also more computationally expensive.

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