Steepest descent, non-analytic roots

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In summary, the conversation is about evaluating the integral I_n(a) as a function of the parameter a in the limit as n approaches infinity, using the method of steepest descent. The question also asks for the result to be expressed in parametric form as a function of the saddle point position. The problem encountered is that the equation for the extremum position cannot be solved analytically. The individual is unsure if the result obtained should be left as is or if there is something missing. They have tried different approaches but have not been successful due to the log(cosh(x)) term in the exponent. They plan to seek help from the TA, but suggestions are still welcome.
  • #1
CompuChip
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Hi,

For a physics class, I am supposed to evaluate the following integral
[tex]I_n(a) = \int_{-\infty}^\infty \mathrm dx \, e^{-n x^2/2 + n a x} \cosh^n(x)[/tex]
as a function of the real non-zero parameter a, in the limit as [itex]n \to \infty[/itex] using the method of steepest descent. The question adds: "Express the ersult in parametric form as a function of the saddle point position."

The problem I ran into, is that the extremum position [itex]x_0[/itex] satisfies the equation
[tex]x_0 - a - \tanh(x_0) = 0[/tex]
which cannot be solved analytically. So I'm getting a bit confused, whether I should just leave the result
[tex]I_n(a) \simeq e^{- n x_0^2 + n a x_0} \sqrt{\frac{2\pi}{f''(x_0)}} [/tex]
where f''(x_0) = tanh(x_0) = x_0 - a, or whether I am missing something here.

I'd appreciate your input.
 
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  • #2
I've tried some more approaches, but in all of them, the problem is in the log(cosh(x)) that's in the exponent. There is no way it can be rewritten without having [itex]\log\cosh(x_0))[/itex], with x0 the position of the minimum of the exponent, in the final expression.

I will try to see the TA tomorrow, but your suggestions are still welcome.
(The exercise was given Thursday afternoon, due on Tuesday).
 

FAQ: Steepest descent, non-analytic roots

What is steepest descent?

Steepest descent is a numerical optimization method used to find the minimum of a function by taking steps in the direction of the steepest slope.

What are non-analytic roots?

Non-analytic roots are roots of a function that cannot be expressed using a finite combination of algebraic operations and elementary functions.

How does steepest descent find non-analytic roots?

Steepest descent can find non-analytic roots by iteratively taking steps in the direction of the steepest slope until the function reaches a local minimum. However, this method may not be efficient for finding non-analytic roots.

What are the limitations of using steepest descent for finding non-analytic roots?

Steepest descent may not be efficient for finding non-analytic roots because it relies on the slope of the function, which can be difficult to calculate for non-analytic functions. Additionally, it may get stuck in local minima and not reach the global minimum.

Are there other methods for finding non-analytic roots?

Yes, there are other methods such as gradient descent, conjugate gradient method, and Newton's method. These methods may be more efficient for finding non-analytic roots compared to steepest descent.

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