Non linear finite differences

In summary, the speaker suggests using an iterative method like Jacobi or Gauss-Seidel and a sparse matrix solver to improve the efficiency and accuracy of solving a non-linear differential equation with a sparse matrix and a changing diagonal matrix. They also suggest considering other numerical methods like finite element or finite volume methods.
  • #1
Pete99
43
0
Hi all,

I am trying to solve a non linear differential equation iteratively using finite differences.

At every iteration I basically have to solve (sorry, for some reason I cannot use the preview function when I write latex, so I'll write in plain text):

Delta_x = (A-J)\b



Where Delta_x is the update I calculate in each iteration, A is a sparse matrix with 5 non zero diagonals and J is diagonal. b and J depend on x, the parameter that I am looking for.

The diagonal matrix J, has only about 10% of its diagonal elements non zero (at fixed positions) and change at every iteration, and A does not change during the iterations.

Right now I calculate the update using the backslash operator in Matlab. My question is if there is any way I can take advantage of the fact that only a small fraction of J changes at every iteration in order to save time.

Thanks,
 
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  • #2


I would suggest considering using an iterative method such as the Jacobi or Gauss-Seidel method to solve your non-linear differential equation. These methods would allow you to take advantage of the fact that only a small fraction of J changes at each iteration, as they only update one element at a time. This can potentially save time and improve the efficiency of your solution.

Additionally, you may want to look into using a sparse matrix solver instead of the backslash operator in Matlab. This can also help save time and improve the accuracy of your solution, especially for large sparse matrices like the one you mentioned.

Finally, I would recommend considering other numerical methods such as finite element or finite volume methods, which may be more suitable for solving non-linear differential equations. These methods can handle complex geometries and boundary conditions, and can also be more efficient for solving non-linear problems.

I hope this helps and good luck with your research!
 

Related to Non linear finite differences

1. What are non linear finite differences?

Non linear finite differences are a numerical method used to approximate the solutions of differential equations. They involve discretizing the continuous domain into a finite number of points and then using a finite difference equation to approximate the derivative at each point.

2. How are non linear finite differences different from linear finite differences?

The main difference between non linear finite differences and linear finite differences is that non linear finite differences involve approximating the derivative at each point using a non linear equation, while linear finite differences use a linear equation. This allows for a more accurate approximation of the solution, especially for non linear differential equations.

3. What are some applications of non linear finite differences?

Non linear finite differences are commonly used in fields such as physics, engineering, and economics to model and solve complex differential equations. They are also used in computer simulations and numerical analysis to approximate solutions for a wide range of problems.

4. What are the advantages of using non linear finite differences?

One of the main advantages of non linear finite differences is their ability to handle non linear differential equations, which are often difficult to solve analytically. They also offer a more accurate approximation of the solution compared to linear finite differences, as well as the ability to handle complex boundary conditions.

5. Are there any limitations to using non linear finite differences?

One limitation of non linear finite differences is that they require a significant amount of computational resources, especially for problems with a large number of points. They also may not be suitable for stiff differential equations, which require a different numerical method for accurate solutions.

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