Understand Adam Moulton & Bashforth Methods for Numerical Analysis

In summary, the conversation discusses numerical analysis and specifically the Runge-Kutta, Adam Bashforth, and Adam Moulton methods. The participants explore the idea behind these methods and their graphical representation. It is suggested to plot the polynomial curves and compare them to the actual path for a better understanding. The concept of predictor-corrector algorithms is also introduced, which involves extrapolating the solution and making corrections based on prior information. There is a mention of a program being written to demonstrate these methods graphically.
  • #1
steven187
176
0
hello all

I have been researching into numerical analysis, differential equations in particular, I underdstand how the Runge kutta methods work geometrically but I don't quit understand what is the idea behind Adam moultons method And Adam Bashforth method, Is there a graphical way of understanding it? I find it very confusing?

steven
 
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  • #2
hello all

so far after a bit more research I have come to understand that the adams bashforth requires information about the solution at more than one point, If I assume that we already know these points then what this method does is find an interpolating polynomial that passes through these points and then uses it to obtain the solution of the next point- this occurs when the resulting polynomial is extrapolated over the next interval h, this is pretty understandable graphically but I still don't understand what adams moultons method does graphically does anybody have any ideas

steven
 
  • #3
Hello Steven,

My understanding of these multi-step methods is that they're similar in that they use previous points to calculate a polynomial curve as a trajectory to the next point as opposed to a "weighted" straight line for Runge-Kutta. However, the Milne's method and Adam-Moulton methods correct the predicted value of the next y before going on to the next step.

I think a nice way of gleaming a geometrical notion of these methods is to work with ODEs that I know what the analytical solution is and then go through the numerical methods and actually plot (in Mathematica of course) the quadratic and cubic curves that are these methods, as well as the "predictors" and "correctors", and see how they follow the actual path.

Why not just work through a few points for y'=Ay. Maybe post some plots too. :smile:
 
  • #4
Hello Saltydog

well that aint a bad idea, there is always one way of finding out how something works and that is if we demonstrated it graphically, I have been writting a program over the last few days, writting the predictor corrector code wasnt difficult but trying to plot each step all on the same graph is a bit complicated, I have given it some thought but i think the way i have structured my program does not accommodate for my expected outcome which is to see the convergence for each point after running the predictor corector many times consecutively for the same point until it satisfies the stopping criteria, anyway this is my current output which is based upon increasing the subintervals

steven
 

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  • #5
hello all

to answer my original question, a predictor-corrector algorithm, consists of basically two parts, the predictor extrapolates the solution over some finite range h based on the information at prior points and is inherently unstable and the corrector which allows for this local instability and makes a correction to the solution at the end of the interval which is also based on prior information as well as the extrapolated solution.

steven
 
  • #6
steven187 said:
Hello Saltydog

well that aint a bad idea, there is always one way of finding out how something works and that is if we demonstrated it graphically, I have been writting a program over the last few days, writting the predictor corrector code wasnt difficult but trying to plot each step all on the same graph is a bit complicated, I have given it some thought but i think the way i have structured my program does not accommodate for my expected outcome which is to see the convergence for each point after running the predictor corector many times consecutively for the same point until it satisfies the stopping criteria, anyway this is my current output which is based upon increasing the subintervals

steven

Hello Steven,

My first though is why didn't you just save the plots in Mathematica as bitmaps and/or convert them to JPEG as I do and then just post the plots directly here without going through Microsoft Office?

Also, the plots seems to have straight line approximations. I was expecting quadratic or cubic approximations for these numerical methods.
 
  • #7
hello saltydog

well the reason why Iv got straight line approximations is because I have structured my program with the 2nd order runge kutta and the second order adam bashforth and second order adam moulton, upgrading the program shouldn't be too difficult anyway

steven
 

1. What is the purpose of the Adam Moulton and Bashforth methods in numerical analysis?

The Adam Moulton and Bashforth methods are numerical techniques used to solve differential equations. They provide a way to approximate the solution to a differential equation at a given set of points, without having to solve the equation analytically. This makes them useful for solving complex and non-linear equations that cannot be easily solved by hand.

2. How do the Adam Moulton and Bashforth methods differ from each other?

The main difference between the two methods is the order of accuracy. The Bashforth method is a one-step method and is accurate to the order of n-1, where n is the number of steps taken. The Adam Moulton method is a two-step method and is accurate to the order of n-2. This means that the Adam Moulton method is more accurate, but also requires more computational effort.

3. How do these methods handle errors and stability?

Both the Adam Moulton and Bashforth methods are known to be stable and have a good error control. This means that they are less likely to produce large errors and are able to maintain accuracy over longer periods of time. However, as with any numerical method, certain conditions may lead to instability and errors may occur. It is important to choose the appropriate method for the specific problem at hand.

4. What are the limitations of the Adam Moulton and Bashforth methods?

One limitation of these methods is that they can only be used for ordinary differential equations (ODEs) and not partial differential equations (PDEs). Additionally, they may not be suitable for all types of ODEs, such as stiff equations, and may require a large number of steps to accurately solve certain problems. It is important to consider the specific characteristics of the problem before choosing these methods.

5. How are the Adam Moulton and Bashforth methods implemented in practice?

Both methods can be implemented using a variety of programming languages and software tools, such as MATLAB or Python. The steps involved in the implementation include discretizing the differential equation, calculating the coefficients for the method, and then using the computed values to approximate the solution at each time step. It is important to understand the theory behind the methods and the specific implementation details to ensure accurate results.

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