Linear multistep methods are used for the numerical solution of ordinary differential equations,
in particular the initial value problem

The Adams-Bashforth methods and Adams-Moulton methods are described on the Linear multistep method page.
There are (at least) two ways that can be used to derive the Adams-Bashforth methods and Adams-Moulton methods.
We will demonstrate the derivations using polynomial interpolation and using Taylor's theorem for
the two-step Adams-Bashforth method.
Derive the two-step Adams-Bashforth method by using polynomial interpolation[edit | edit source]
From the w:Fundamental theorem of Calculus we can get
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(1)
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Set
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(2)
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To get the value of
, we can use an interpolating polynomial
as an approximation of
.
The interpolation polynomial in the Lagrange form is a linear combination

where

Then the interpolation can be

Thus, (2) becomes
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(3)
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Integrating and simplifying, the right hand side of equation (3) becomes

Since
,
and
are equally spaced, then
.
Therefore, the value of
is

Putting this value back to (1) yields

Thus, the equation
is the two-step Adams-Bashforth method.
Derive the two-step Adams-Bashforth method by using Taylor's theorem[edit | edit source]
To simplify, let's set
.
Then the general form of Adams-Bashforth method is
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(4)
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where
.
For the two-step Adams-Bashforth method, let's set
. Then (4) becomes

By using Taylor's theorem, expand
at
to get

Thus, the simplified form is
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(5)
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Expanding
at
yields
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(6)
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Subtracting (5) from (6) and then requiring the h^2 term to cancel makes
.
The two-step Adams-Bashforth method is then

Since

the local truncation error is of order
and thus the method is second order.
(See w: Linear multistep method#Consistency and order and w:Truncation error)
1. Derive three-step Adams-Bashforth method by using polynomial interpolation
The initial problem is
Then we can get:
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(8)
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let's set:
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(9)
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Then,we can use P(t) as an interpolation of f(t,y(t)). To derive the three-step Adams-bashforth method, the interpolation polynomial is:
Since
,
,
and
are equally spaced, then
.
So the integral of each part of P(t) is :
Thus the approximate value of A is
Put this value back to (8)
Thus, the three-step Adams-Bashforth method is
2. Derive the second-order Adams-Moulton method by using Taylor's theorem
To simplify, note
to represent
.From Taylor expansion of the binary function (w:Taylor's theorem):
Then by using:
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(10)
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can get:
Therefore, simplify this equation to get:
Thus, subtitute
into (10):
Combining the equation:
and Taylor expansion:
can get
. Therefore, the second-order Adams-moulton method is:
Predictor–corrector method[edit | edit source]
To solve an ordinary differential equation (ODE), a w:Predictor–corrector method is an algorithm that can be used in two steps. First, the prediction step calculates a rough approximation of the desired quantity, typically using an explicit method. Second, the corrector step refines the initial approximation using another means, typically an implicit method.
Here mainly discuss about using Adams-bashforth and Adams-moulton methods as a pair to construct a predictor–corrector method.
Example: Adams predictor–corrector method[edit | edit source]
Let's start from the two-step Adams method. The prediction step is to use two-step Adams-bashforth:
Then, by using two-step Adams-moulton the corrector step can be:
Also, by using four-step Adams-bashforth and Adams-moulton methods together, the predictor-corrector formula is:
Note, the four-step Adams-bashforth method needs four initial values to start the calculation. It needs to use other methods, for example Runge-Kutta, to get these initial values.
Four-step Adams predictor-corrector method:
function [x y]=maadams4(dyfun,xspan,y0,h)
% use four-step Adams predictor-corrector method to solve an ODE y'=f(x,y), y(x0)=y0
% inputs: dyfun -- the function f(x,y), as an inline
% xspan -- the interval [x0,xn]
% y0 -- the initial value
% h -- the step size
% output: x, y -- the node and the value of y
x=xspan(1):h:xspan(2);
% use Runge-Kutta method to get four initial values
[xx,yy]=marunge(dyfun,[x(1),x(4)],y0,h);
y(1)=yy(1);y(2)=yy(2);
y(3)=yy(3);y(4)=yy(4);
for n=4:(length(x)-1)
p=y(n)+h/24*(55*feval(dyfun,x(n),y(n))-59*feval(dyfun,x(n-1),y(n-1))+37*feval(dyfun,x(n-2),y(n-2))-9*feval(dyfun,x(n-3),y(n-3)));
y(n+1)=y(n)+h/24*(9*feval(dyfun,x(n+1),p)+19*feval(dyfun,x(n),y(n))-5*feval(dyfun,x(n-1),y(n-1))+feval(dyfun,x(n-2),y(n-2)));
end
x=x';y=y';
Runge-Kutta method:
function [x y]=marunge(dyfun,xspan,y0,h)
x=xspan(1):h:xspan(2);
y(1)=y0;
for n=1:(length(x)-1)
k1=feval(dyfun,x(n),y(n));
k2=feval(dyfun,x(n)+h/2,y(n)+h/2*k1);
k3=feval(dyfun,x(n)+h/2,y(n)+h/2*k2);
k4=feval(dyfun,x(n+1),y(n)+h*k3);
y(n+1)=y(n)+h*(k1+2*k2+2*k3+k4)/6;
end
x=x';y=y'