Solving Unknowns in MATLAB: Approximation Scheme

In summary, using a pronumeral without assigning a value to it does not seem to be an option in MATLAB.
  • #1
ballzac
104
0
Hi. I need to run through an approximation scheme, and one of the variables in the scheme is unknown, but I know what the final answer should be. I want to calculate the value of the unknown by figuring out what it must be for the approximation to equal the expected result after a significant number of iterations. Is there, in MATLAB, some way to use a pronumeral without assigning a value to it? i.e. so that the pronumeral remains in the result. e.g. we might run ten iterations of the program and have a^10 as a factor in the final result, then run it for 1000 iterations and have a^1000 in the final result. I have not been able to figure this out. Maybe there is a 'trick' that can be used for this kind of problem? Thanks in advance.
 
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  • #2
ballzac said:
Hi. I need to run through an approximation scheme, and one of the variables in the scheme is unknown, but I know what the final answer should be. I want to calculate the value of the unknown by figuring out what it must be for the approximation to equal the expected result after a significant number of iterations. Is there, in MATLAB, some way to use a pronumeral without assigning a value to it? i.e. so that the pronumeral remains in the result. e.g. we might run ten iterations of the program and have a^10 as a factor in the final result, then run it for 1000 iterations and have a^1000 in the final result. I have not been able to figure this out. Maybe there is a 'trick' that can be used for this kind of problem? Thanks in advance.

Not that I'm aware of. Maybe with the symbolic toolbox? But regardless, you should probably be reformulating so you do some kind of Newton-Raphson or some other sort of fixed-point iteration?
http://en.wikipedia.org/wiki/Fixed_point_iteration
http://elm.eeng.dcu.ie/~ee317/Matlab_Examples/iter/tutinfo[1].htm
 
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  • #3
I have a question that has one known value at each end (boundary conditions), and it is required to use one known value, and use that and the next value (which is unknown), and then follow the recurrence relation until the other known value is reached, and then use this value to calculate what the unknown value is. I hope that made sense. I am a little tired. So anyway, I'm sure there would be many ways to solve it, but that is specifically what we were told to do, and we were told to use MATLAB. Not sure how it was meant to be done. Oh well, I left it too late as it is due tomorrow, and I'm going to bed now, lol. Thanks for the info anyway. Cheers.
 
  • #4
Yes, this would be possible with the symbolic math toolbox.

If you don't have that, maybe you can normalize everything to a (use a = 1 up until your final step), then re-scale? I don't think I fully understand your problem...

-Kerry
 
  • #5
KLoux said:
Yes, this would be possible with the symbolic math toolbox.

If you don't have that, maybe you can normalize everything to a (use a = 1 up until your final step), then re-scale? I don't think I fully understand your problem...

-Kerry
I don't think I really do either :rolleyes: :redface:. But anyway, I think you're right that I will have to add it at the end. I'll just have to keep track of what happens to it at each iteration.

I gather we are not meant to use the symbolic math toolbox because AFAIK it is not installed at uni and has never been mentioned in the course. Thanks for the suggestion, I will see how it goes.
 

Related to Solving Unknowns in MATLAB: Approximation Scheme

1. How does the approximation scheme work in MATLAB?

The approximation scheme in MATLAB is a method used to estimate unknown values based on given data. It involves using mathematical algorithms and techniques, such as interpolation or curve fitting, to create a model that closely approximates the data. This model can then be used to make predictions or fill in missing values for unknown data points.

2. What types of unknowns can be solved using the approximation scheme in MATLAB?

The approximation scheme in MATLAB can be used to solve various types of unknowns, such as missing data points in a dataset, unknown parameters in a mathematical model, or even functions with unknown values. It is a versatile tool that can be applied to a wide range of problems in different fields of science and engineering.

3. How accurate are the solutions obtained using the approximation scheme in MATLAB?

The accuracy of the solutions obtained using the approximation scheme in MATLAB depends on various factors, such as the quality and quantity of the given data, the type of approximation method used, and the complexity of the problem. Generally, the accuracy of the solutions improves as more data points are used and more sophisticated approximation methods are applied.

4. Can the approximation scheme in MATLAB handle noisy or incomplete data?

Yes, the approximation scheme in MATLAB can handle noisy or incomplete data. In fact, it is designed to handle such situations by using techniques that can smooth out noise or fill in missing values. However, the accuracy of the solutions may be affected by the level of noise or missing data in the original dataset.

5. Is there any limit to the size or complexity of the problem that can be solved using the approximation scheme in MATLAB?

There is no specific limit to the size or complexity of the problem that can be solved using the approximation scheme in MATLAB. However, as the problem size or complexity increases, the computational time and resources required to obtain a solution may also increase. It is always recommended to optimize the problem formulation and choose appropriate approximation methods to achieve efficient and accurate solutions.

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