Numerical method to Lippman-Schwinger equation

In summary, this sums up to saying that you can re-order terms in a finite sum without changing the result.
  • #1
HadronPhysics
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There is a question that puzzle me when I apply numerical method to principal value integral. Let me descibe it. We make use of the fact that the integral ##\int_0^\infty \frac{dk}{k^2-k_0^2}## vanishes, namely,
$$
\int_0^\infty \frac{dk}{k^2-k_0^2} = 0 .
$$
We use this formula to express a principal value intergral as
$$ \mathcal{P}\int_0^\infty \frac{f(x)}{k^2-k_0^2}dk = \int_0^\infty \frac{f(k)-f(k_0)}{k^2-k_0^2}dk .$$
Now, the right hand side is no longer singular at ##k=k_0## because it is proportional to the derivative ##df/dx##. We can approximate this integral numerically, i.e.,
$$ \int_0^\infty \frac{f(k)-f(k_0)}{k^2-k_0^2}dk \approx \sum_i^N w_i \frac{f(k_i)-f(k_0)}{k_i^2-k_0^2} ,$$
where we adopt the Gaussian quadrature method.
Next, we change to Lippman-Schwinger equation where the principal integral occurs. That is
$$ R(k', k) = V(k', k) + \frac{2}{\pi} \mathcal{P}\int_0^\infty dp \frac{p^2V(k', p)R(p, k)}{(k_0^2-p^2)/2\mu} .$$
Then, we can evaluate this equation by the method that we have mentioned. we get
$$ R(k, k_0) = V(k, k_0) + \frac{2}{\pi} \sum_i^N \frac{p_i^2V(k', p_i)R(p_i, k_0)-k_0^2V(k', k_0)R(k_0, k_0)}{(k_0^2-p_i^2)/2\mu} w_i ,$$
where we let $k$ be $k_0$.
At present, everything is ok. the question that puzzles me will occur at the next step. In some computational physics books, for example, you can refer to [[1]](#id1), page: 118, it said that we can split term in summation to two part, namely,
$$ R(k, k_0) = V(k, k_0) \frac{2}{\pi} \left[ \sum_i^N \frac{k_i^2V(k, k_i)R(k_i, k_0)w_i}{(k_0^2-k_i^2)/2\mu} - k_0^2V(k, k_0)R(k_0, k_0)\sum_j^N\frac{w_j}{(k_0^2-k_j^2)/2\mu} \right] .$$
In the previous discussion, we constructed the term $\frac{f(k)-f(k_0)}{k^2-k_0^2}$ to avoid the singular at $k=k_0$. But here, we split the summation into two part. If $k_j\to k_0$, or $k_i\to k_0$, we can not see the term that is proportional to $df/dk$. I can not understand this step, because I think it contradicts the eqaution: ##\mathcal{P}\int_0^\infty \frac{f(x)}{k^2-k_0^2}dk = \int_0^\infty \frac{f(k)-f(k_0)}{k^2-k_0^2}dk. ##<div id="id1"></div>
- [1] [COMPUTATIONAL PHYSICS](https://courses.physics.ucsd.edu/2017/Spring/physics142/Lectures/Lecture18/Hjorth-JensenLectures2010.pdf)
 
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  • #2
You have approximated the integral by a finite sum of bounded quantities. Terms of finite sums can always be re-ordered as you find convenient without affecting the result, and doing so in a context where you are using floating-point arithmetic might actually increase the accuracy of the result.

You are not then taking the limit [itex]N \to \infty[/itex] and trying to sum an infinite series, where I agree that any re-ordering of the terms would require rigorous jusitification that the limit is not thereby affected. (It is more complicated here, since the values of the summands are themselves dependent on [itex]N[/itex].)
 
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FAQ: Numerical method to Lippman-Schwinger equation

What is the Lippman-Schwinger equation?

The Lippman-Schwinger equation is an integral equation used in quantum mechanics to describe the scattering process. It relates the scattered wave function to the incident wave function and the interaction potential. It is often used to solve problems in quantum scattering theory, particularly in the context of non-relativistic particles.

Why are numerical methods needed to solve the Lippman-Schwinger equation?

Numerical methods are needed because the Lippman-Schwinger equation is generally difficult to solve analytically, especially for complex potentials or higher-dimensional problems. Numerical techniques allow us to approximate the solution with a high degree of accuracy, making it possible to study systems that are otherwise intractable.

What are some common numerical methods used to solve the Lippman-Schwinger equation?

Common numerical methods include the Born series expansion, finite difference methods, and iterative techniques such as the Conjugate Gradient method. Additionally, spectral methods and discretization techniques like the Galerkin method are often employed to convert the integral equation into a solvable system of linear equations.

What are the challenges associated with numerical solutions to the Lippman-Schwinger equation?

Challenges include handling singularities in the integral kernel, ensuring numerical stability, and achieving convergence. Additionally, the computational cost can be significant for large systems or high-dimensional problems. Efficient algorithms and high-performance computing resources are often required to address these challenges.

How can the accuracy of numerical solutions to the Lippman-Schwinger equation be verified?

The accuracy can be verified by comparing numerical results with known analytical solutions for simple cases, performing convergence tests, and checking the consistency of the solutions with physical expectations. Additionally, error analysis and validation against experimental data can provide further confidence in the numerical results.

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