Is this approximation approach applicable?

In summary, the conversation discusses a method for solving a one dimensional potential field with multiple regions and coefficients. The approach involves using recursion to solve smaller instances of the problem in each region, and then combining the solutions to obtain the overall solution. This is a commonly used technique in physics and engineering for dealing with complex problems with multiple boundary conditions.
  • #1
zhanhai
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A one dimensional potential field V(x), and its solution of SE, is divided into three regions. And the solution has two coefficients in each of these regions. There are six boundary conditions: two on each of the boundaries, plus two global boundary conditions.

Now, as I wish to refine the solution in the two side regions by introducing two new components (base vectors) in each of the two side regions, there would be totally ten coefficients, which is more than the 6 boundary conditions.

I would think of an approach like this:

Keeping the solution in the center region and one side region intact, and in the remaining side region the two new components along with their new coefficients are added. In this way, we have four coefficients to be determined in this region, with four available boundary conditions (two on its boundary, plus the two global). So the four coefficients can be determined.

Next, we would do the same for the other side region. (Or, we could also "loosen" the two original coefficients in the central region (but without adding new components/coefficients) and find their anewed values.)

Is this applicable? And, is this the so-called "recursion"?
 
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  • #2


Yes, this approach is applicable and it can be considered a form of recursion. Recursion is a problem-solving technique where the solution to a larger problem can be obtained by solving smaller instances of the same problem. In this case, the larger problem is finding the solution to the entire one dimensional potential field, and the smaller instances are the individual regions with their own coefficients and boundary conditions. By solving for the coefficients in each region separately and then combining them, you are using recursion to find the solution to the larger problem. This approach is commonly used in physics and engineering to solve complex problems with multiple boundary conditions.
 

FAQ: Is this approximation approach applicable?

Is this approximation approach applicable for all types of data?

No, the applicability of an approximation approach depends on the type of data and the specific problem it is being applied to. Some approaches may work well for continuous data, while others may be more suitable for discrete data.

How do I know if an approximation approach is the best method for my data?

It is important to first understand the nature of your data and the problem you are trying to solve. Then, research and compare different approximation approaches to determine which one is most suitable for your data and will yield the most accurate results.

Can I use multiple approximation approaches for the same data?

Yes, in some cases it may be beneficial to use multiple approximation approaches to get a more accurate result. However, this may also increase the complexity and time required for analysis.

How do I validate the results of an approximation approach?

Validation of approximation results can be done through cross-validation techniques, where the data is split into subsets and the approximation is performed on each subset, and then compared to the actual results. Other methods include using different metrics to evaluate the accuracy of the approximation.

Are there any limitations to using approximation approaches?

Yes, there are limitations to using approximation approaches, such as the assumption of linearity in data, potential biases in the data, and the need for a sufficient amount of data for accurate approximation. It is important to carefully consider these limitations when selecting and using an approximation approach.

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