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Mar 24, 2021 at 14:26 comment added Mithridates the Great @k.dkhk Well, a rough explanation is that the accuracy of your solution is in the order of $h^{4}$ and the error that you are getting due to truncating terms of higher orders is in the order of $h^{5}$. In another word, $h^{4}$ is a really rough estimation of your accuracy and $h^{5}$ is a rough estimation of your error getting out of this stencil.
Mar 24, 2021 at 14:22 comment added k.dkhk @AloneProgrammer ok thanks. My theoritical knowledge is very poor so excuse for asking stupid. What is the difference between between those orders? (order of accuracy vs order of error). thanks.
Mar 24, 2021 at 13:34 history edited Mithridates the Great CC BY-SA 4.0
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Mar 24, 2021 at 13:33 comment added Mithridates the Great @k.dkhk I think you are right it is 4 order accurate but the error is in the order of 5.
Mar 24, 2021 at 13:31 comment added k.dkhk @AloneProgrammer I have implemented the method. It seems like it is order 4. Are certain that this is order 5?
Mar 23, 2021 at 21:58 comment added k.dkhk @AloneProgrammer I understand the exact arguement for saying the order is 5? I know that our Taylors have error term of $O(h^5)$ but what is the reason that we can conclude that final stencils are fifth order accurate?
May 2, 2020 at 16:36 vote accept econmajorr
Mar 27, 2020 at 13:44 comment added Mithridates the Great @econmajorr what do you mean using scipy? for any given $\alpha$ and $\beta$ the procedure is the same as here but you need to solve $\alpha + \beta + 1$ linear equations. So, if you are asking to solve this linear equations numerically by using scipy.linalg, the answer is: It might be possible, but you need to try it to know for sure.
Mar 26, 2020 at 8:13 comment added econmajorr thank you for this very thorough answer! Simple question: Can I use scipy package to actualy give me the optimal coefficients for any set of $\alpha, \beta$?
Mar 25, 2020 at 5:01 history edited Mithridates the Great CC BY-SA 4.0
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Mar 25, 2020 at 4:03 history answered Mithridates the Great CC BY-SA 4.0