The Numerical Differential Equation Analysis package combines functionality for analyzing differential equations using Butcher trees, Gaussian quadrature, and NewtonCotes quadrature.
Contents
RungeKutta methods are useful for numerically solving certain types of ordinary differential equations. Deriving highorder RungeKutta methods is no easy task, however. There are several reasons for this. The first difficulty is in finding the socalled order conditions. These are nonlinear equations in the coefficients for the method that must be satisfied to make the error in the method of order O (hn) for some integer n where h is the step size. The second difficulty is in solving these equations. Besides being nonlinear, there is generally no unique solution, and many heuristics and simplifying assumptions are usually made. Finally, there is the problem of combinatorial explosion. For a twelfthorder method there are 7813 order conditions!
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Create orderThis package performs the first task: finding the order conditions that must be satisfied. The result is expressed in terms of unknown coefficients aij, bj, and ci. The sstage RungeKutta method to advance from x to x+h is then
where
Sums of the elements in the rows of the matrix [aij] occur repeatedly in the conditions imposed on aij and bj. In recognition of this and as a notational convenience it is usual to introduce the coefficients ci and the definition
This definition is referred to as the rowsum condition and is the first in a sequence of rowsimplifying conditions.
If aij=0 for all i≤j the method is explicit; that is, each of the Yi (x+h) is defined in terms of previously computed values. If the matrix [aij] is not strictly lower triangular, the method is implicit and requires the solution of a (generally nonlinear) system of equations for each timestep. A diagonally implicit method has aij=0 for all i<j.
There are several ways to express the order conditions. If the number of stages s is specified as a positive integer, the order conditions are expressed in terms of sums of explicit terms. If the number of stages is specified as a symbol, the order conditions will involve symbolic sums. If the number of stages is not specified at all, the order conditions will be expressed in stageindependent tensor notation. In addition to the matrix a and the vectors b and c, this notation involves the vector e, which is composed of all ones. This notation has two distinct advantages: it is independent of the number of stages s and it is independent of the particular RungeKutta method.
For further details of the theory see the references.
ai,j 
the coefficient of f(Yj(x)) in the formula for Yi(x) of the method 
bj 
the coefficient of f(Yj(x)) in the formula for Y(x) of the method 
ci 
a notational convenience for aij 
e 
a notational convenience for the vector (1, 1, 1, …) 
Notation used by functions for Butcher.
RungeKuttaOrderConditions[p,s] 
give a list of the order conditions that any sstage RungeKutta method of order p must satisfy 
ButcherPrincipalError[p,s] 
give a list of the order p+1 terms appearing in the Taylor series expansion of the error for an orderp, sstage RungeKutta method 
RungeKuttaOrderConditions[p], ButcherPrincipalError[p] 
give the result in stageindependent tensor notation 
Functions associated with the order conditions of RungeKutta methods.
ButcherRowSum 
specify whether the rowsum conditions for the ci should be explicitly included in the list of order conditions 
ButcherSimplify 
specify whether to apply Butcher’s row and column simplifying assumptions 
Some options for RungeKuttaOrderConditions.
This gives the number of order conditions for each order up through order 10. Notice the combinatorial explosion.

This gives the order conditions that must be satisfied by any firstorder, 3stage RungeKutta method, explicitly including the rowsum conditions.

These are the order conditions that must be satisfied by any secondorder, 3stage RungeKutta method. Here the rowsum conditions are not included.

It should be noted that the sums involved on the lefthand sides of the order conditions will be left in symbolic form and not expanded if the number of stages is left as a symbolic argument. This will greatly simplify the results for highorder, manystage methods. An even more compact form results if you do not specify the number of stages at all and the answer is given in tensor form.
These are the order conditions that must be satisfied by any secondorder, sstage method.

Replacing s by 3 gives the same result asRungeKuttaOrderConditions.

These are the order conditions that must be satisfied by any secondorder method. This uses tensor notation. The vector e is a vector of ones whose length is the number of stages.

The tensor notation can likewise be expanded to give the conditions in full.

These are the principal error coefficients for any thirdorder method.

This is a bound on the local error of any thirdorder method in the limit as h approaches 0, normalized to eliminate the effects of the ODE.

Here are the order conditions that must be satisfied by any fourthorder, 1stage RungeKutta method. Note that there is no possible way for these order conditions to be satisfied; there need to be more stages (the second argument must be larger) for there to be sufficiently many unknowns to satisfy all of the conditions.

RungeKuttaMethod 
specify the type of RungeKutta method for which order conditions are being sought 
Explicit 
a setting for the option RungeKuttaMethod specifying that the order conditions are to be for an explicit RungeKutta method 
DiagonallyImplicit 
a setting for the option RungeKuttaMethod specifying that the order conditions are to be for a diagonally implicit RungeKutta method 
Implicit 
a setting for the option RungeKuttaMethod specifying that the order conditions are to be for an implicit RungeKutta method 
$RungeKuttaMethod 
a global variable whose value can be set to Explicit, DiagonallyImplicit, or Implicit 
Controlling the type of RungeKutta method in RungeKuttaOrderConditions and related functions.
RungeKuttaOrderConditions and certain related functions have the option RungeKuttaMethod with default setting $RungeKuttaMethod. Normally you will want to determine the RungeKutta method being considered by setting $RungeKuttaMethod to one of Implicit, DiagonallyImplicit, and Explicit, but you can specify an option setting or even change the default for an individual function.
These are the order conditions that must be satisfied by any secondorder, 3stage diagonally implicit RungeKutta method.

An alternative (but less efficient) way to get a diagonally implicit method is to force a to be lower triangular by replacing uppertriangular elements with 0.

These are the order conditions that must be satisfied by any thirdorder, 2stage explicit RungeKutta method. The contradiction in the order conditions indicates that no such method is possible, a result which holds for any explicit RungeKutta method when the number of stages is less than the order.

ButcherColumnConditions[p,s] 
give the column simplifying conditions up to and including order p for s stages 
ButcherRowConditions[p,s] 
give the row simplifying conditions up to and including order p for s stages 
ButcherQuadratureConditions[p,s] 
give the quadrature conditions up to and including order p for s stages 
ButcherColumnConditions[p], ButcherRowConditions[p], etc. 
give the result in stageindependent tensor notation 
More functions associated with the order conditions of RungeKutta methods.
Butcher showed that the number and complexity of the order conditions can be reduced considerably at high orders by the adoption of socalled simplifying assumptions. For example, this reduction can be accomplished by adopting sufficient row and column simplifying assumptions and quadraturetype order conditions. The option ButcherSimplify in RungeKuttaOrderConditions can be used to determine these automatically.
These are the column simplifying conditions up to order 4.

These are the row simplifying conditions up to order 4.

These are the quadrature conditions up to order 4.

Trees are fundamental objects in Butcher’s formalism. They yield both the derivative in a power series expansion of a RungeKutta method and the related order constraint on the coefficients. This package provides a number of functions related to Butcher trees.
f 
the elementary symbol used in the representation of Butcher trees 
ButcherTrees[p] 
give a list, partitioned by order, of the trees for any RungeKutta method of order p 
ButcherTreeSimplify[p,,] 
give the set of trees through order p that are not reduced by Butcher’s simplifying assumptions, assuming that the quadrature conditions through order p, the row simplifying conditions through order , and the column simplifying conditions through order all hold. The result is grouped by order, starting with the first nonvanishing trees 
ButcherTreeCount[p] 
give a list of the number of trees through order p 
ButcherTreeQ[tree] 
give True if the tree or list of trees tree is valid functional syntax, and False otherwise 
Constructing and enumerating Butcher trees.
This gives the trees that are needed for any thirdorder method. The trees are represented in a functional form in terms of the elementary symbol f.

This tests the validity of the syntax of two trees. Butcher trees must be constructed using multiplication, exponentiation or application of the function f.

This evaluates the number of trees at each order through order 10. The result is equivalent to Out[2] but the calculation is much more efficient since it does not actually involve constructing order conditions or trees.

The previous result can be used to calculate the total number of trees required at each order through order10.

The number of constraints for a method using row and column simplifying assumptions depends upon the number of stages. ButcherTreeSimplify gives the Butcher trees that are not reduced assuming that these assumptions hold.
This gives the additional trees that are necessary for a fourthorder method assuming that the quadrature conditions through order 4 and the row and column simplifying assumptions of order 1 hold. The result is a single tree of order 4 (which corresponds to a single fourthorder condition).

It is often useful to be able to visualize a tree or forest of trees graphically. For example, depicting trees yields insight, which can in turn be used to aid in the construction of RungeKutta methods.
ButcherPlot[tree] 
give a plot of the tree tree 
ButcherPlot[{tree1,tree2,…}] 
give an array of plots of the trees in the forest {tree1, tree2,…} 
Drawing Butcher trees.
ButcherPlotColumns 
specify the number of columns in the GraphicsGrid plot of a list of trees 
ButcherPlotLabel 
specify a list of plot labels to be used to label the nodes of the plot 
ButcherPlotNodeSize 
specify a scaling factor for the nodes of the trees in the plot 
ButcherPlotRootSize 
specify a scaling factor for the highlighting of the root of each tree in the plot; a zero value does not highlight roots 
Options to ButcherPlot.
This plots and labels the trees through order 4.

In addition to generating and drawing Butcher trees, many functions are provided for measuring and manipulating them. For a complete description of the importance of these functions, see Butcher.
ButcherHeight[tree] 
give the height of the tree tree 
ButcherWidth[tree] 
give the width of the tree tree 
ButcherOrder[tree] 
give the order, or number of vertices, of the tree tree 
ButcherAlpha[tree] 
give the number of ways of labeling the vertices of the tree tree with a totally ordered set of labels such that if (m, n) is an edge, then m<n 
ButcherBeta[tree] 
give the number of ways of labeling the tree tree with ButcherOrder[tree]1 distinct labels such that the root is not labeled, but every other vertex is labeled 
ButcherBeta[n,tree] 
give the number of ways of labeling n of the vertices of the tree with n distinct labels such that every leaf is labeled and the root is not labeled 
ButcherBetaBar[tree] 
give the number of ways of labeling the tree tree with ButcherOrder[tree] distinct labels such that every node, including the root, is labeled 
ButcherBetaBar[n,tree] 
give the number of ways of labeling n of the vertices of the tree with n distinct labels such that every leaf is labeled 
ButcherGamma[tree] 
give the density of the tree tree; the reciprocal of the density is the righthand side of the order condition imposed by tree 
ButcherPhi[tree,s] 
give the weight of the tree tree; the weight (tree) is the lefthand side of the order condition imposed by tree 
ButcherPhi[tree] 
give (tree) using tensor notation 
ButcherSigma[tree] 
give the order of the symmetry group of isomorphisms of the tree tree with itself 
Other functions associated with Butcher trees.
This gives the order of the tree f[f[f[f] f^2]].

This gives the density of the tree f[f[f[f] f^2]].

This gives the elementary weight function imposed by f[f[f[f] f^2]] for an sstage method.

The subscript notation is a formatting device and the subscripts are really just the indexed variable NumericalDifferentialEquationAnalysis`Private`$i.

It is also possible to obtain solutions to the order conditions using Solve and related functions. Many issues related to the construction RungeKutta methods using this package can be found in Sofroniou. The article also contains details concerning algorithms used in Butcher.m and discusses applications.
Gaussian Quadrature
As one of its methods, the Mathematica function NIntegrate uses a fairly sophisticated GaussKronrodbased algorithm. The Gaussian quadrature functionality provided in Numerical Differential Equation Analysis allows you to easily study some of the theory behind ordinary Gaussian quadrature which is a little less sophisticated.
The basic idea behind Gaussian quadrature is to approximate the value if an integral as a linear combination of values of the integrand evaluated at specific points:
Since there are 2n free parameters to be chosen (both the abscissas xi and the weights wi) and since both integration and the sum are linear operations, you can expect to be able to make the formula correct for all polynomials of degree less than about 2n. In addition to knowing what the optimal abscissas and weights are, it is often desirable to know how large the error in the approximation will be. This package allows you to answer both of these questions.
GaussianQuadratureWeights[n,a,b] 
give a list of the pairs (xi, wi) to machine precision for quadrature on the interval a to b 
GaussianQuadratureError[n,f,a,b] 

give the error to machine precision 

GaussianQuadratureWeights[n,a,b,prec] 

give a list of the pairs (xi, wi) to precision prec 

GaussianQuadratureError[n,f,a,b,prec] 

give the error to precision prec 

Finding formulas for Gaussian quadrature.
This gives the abscissas and weights for the fivepoint Gaussian quadrature formula on the interval (3, 7).

Here is the error in that formula. Unfortunately it involves the tenth derivative of f at an unknown point so you don’t really know what the error itself is.

You can see that the error decreases rapidly with the length of the interval.

NewtonCotes
As one of its methods, the Mathematica function NIntegrate uses a fairly sophisticated GaussKronrod based algorithm. Other types of quadrature formulas exist, each with their own advantages. For example, Gaussian quadrature uses values of the integrand at oddly spaced abscissas. If you want to integrate a function presented in tabular form at equally spaced abscissas, it won’t work very well. An alternative is to use NewtonCotes quadrature.
The basic idea behind NewtonCotes quadrature is to approximate the value of an integral as a linear combination of values of the integrand evaluated at equally spaced points:
In addition, there is the question of whether or not to include the end points in the sum. If they are included, the quadrature formula is referred to as a closed formula. If not, it is an open formula. If the formula is open there is some ambiguity as to where the first abscissa is to be placed. The open formulas given in this package have the first abscissa one half step from the lower end point.
Since there are n free parameters to be chosen (the weights) and since both integration and the sum are linear operations, you can expect to be able to make the formula correct for all polynomials of degree less than about n. In addition to knowing what the weights are, it is often desirable to know how large the error in the approximation will be. This package allows you to answer both of these questions.
NewtonCotesWeights[n,a,b] 
give a list of the n pairs (xi, wi) for quadrature on the interval a to b 
NewtonCotesError[n,f,a,b] 
give the error in the formula 
Finding formulas for NewtonCotes quadrature.
option name 
default value 

QuadratureType 
Closed 
the type of quadrature, Open or Closed 
Option for NewtonCotesWeights and NewtonCotesError.
Here are the abscissas and weights for the fivepoint closed NewtonCotes quadrature formula on the interval (3, 7).

Here is the error in that formula. Unfortunately it involves the sixth derivative of f at an unknown point so you don’t really know what the error itself is.

You can see that the error decreases rapidly with the length of the interval.

This gives the abscissas and weights for the fivepoint open NewtonCotes quadrature formula on the interval (3, 7).

Here is the error in that formula.

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In numerical analysis, the RungeKutta methods (German pronunciation:[ˌʀʊŋəˈkʊta]) are an important family of implicit and explicit iterative methods for the approximation of solutions of ordinary differential equations. These techniques were developed around 1900 by the German mathematicians C. Runge and M.W. Kutta.
See the article on numerical ordinary differential equations for more background and other methods. See also List of RungeKutta methods.
Contents1 The common fourthorder RungeKutta method 2 Explicit RungeKutta methods o 2.1 Examples 3 Usage 4 Adaptive RungeKutta methods 5 Implicit RungeKutta methods 6 References 7 External links 
One member of the family of RungeKutta methods is so commonly used that it is often referred to as “RK4”, “classical RungeKutta method” or simply as “the RungeKutta method”.
Let an initial value problem be specified as follows.
Then, the RK4 method for this problem is given by the following equations:
where yn + 1 is the RK4 approximation of y(tn + 1), and
Thus, the next value (yn + 1) is determined by the present value (yn) plus the product of the size of the interval (h) and an estimated slope. The slope is a weighted average of slopes:
k1 is the slope at the beginning of the interval;
k2 is the slope at the midpoint of the interval, using slope k1 to determine the value of y at the point tn + h / 2 using Euler’s method;
k3 is again the slope at the midpoint, but now using the slope k2 to determine the yvalue;
k4 is the slope at the end of the interval, with its yvalue determined using k3.
In averaging the four slopes, greater weight is given to the slopes at the midpoint:
The RK4 method is a fourthorder method[needs reference], meaning that the error per step is on the order of h5, while the total accumulated error has order h4.
Note that the above formulae are valid for both scalar and vectorvalued functions (i.e., y can be a vector and f an operator). For example one can integrate Schrödinger’s equation using the Hamiltonian operator as function f.
The family of explicit RungeKutta methods is a generalization of the RK4 method mentioned above. It is given by
where
(Note: the above equations have different but equivalent definitions in different texts).
To specify a particular method, one needs to provide the integer s (the number of stages), and the coefficients aij (for 1 ≤ j < i ≤ s), bi (for i = 1, 2, …, s) and ci (for i = 2, 3, …, s). These data are usually arranged in a mnemonic device, known as a Butcher tableau (after John C. Butcher):
0 

c2 
a21 

c3 
a31 
a32 

cs 
as1 
as2 
as,s − 1 

b1 
b2 
bs − 1 
bs 

The RungeKutta method is consistent if
There are also accompanying requirements if we require the method to have a certain order p, meaning that the truncation error is O(hp+1). These can be derived from the definition of the truncation error itself. For example, a 2stage method has order 2 if b1 + b2 = 1, b2c2 = 1/2, and b2a21 = 1/2.
The RK4 method falls in this framework. Its tableau is:
0 

1/2 
1/2 

1/2 
0 
1/2 

1 
0 
0 
1 

1/6 
1/3 
1/3 
1/6 

However, the simplest RungeKutta method is the (forward) Euler method, given by the formula yn + 1 = yn + hf(tn,yn). This is the only consistent explicit RungeKutta method with one stage. The corresponding tableau is:
0 

1 
An example of a secondorder method with two stages is provided by the midpoint method
The corresponding tableau is:
0 

1/2 
1/2 

0 
1 

Note that this ‘midpoint’ method is not the optimal RK2 method. An alternative is provided by Heun’s method, where the 1/2’s in the tableau above are replaced by 1’s and the b’s row is [1/2, 1/2]. If one wants to minimize the truncation error, the method below should be used (Atkinson p.423). Other important methods are Fehlberg, CashKarp and DormandPrince. Also, read the article on Adaptive Stepsize.
The following is an example usage of a twostage explicit RungeKutta method:
0 

2/3 
2/3 

1/4 
3/4 

to solve the initialvalue problem
with step size h=0.025.
The tableau above yields the equivalent corresponding equations below defining the method:
k1 = yn
t0 = 1 

y0 = 1 

t1 = 1.025 

k1 = y0 = 1 
f(t0,k1) = 2.557407725 
k2 = y0 + 2 / 3hf(t0,k1) = 1.042623462 

y1 = y0 + h(1 / 4 f(t0,k1) + 3 / 4 f(t0 + 2 / 3h,k2)) = 1.066869388 

t2 = 1.05 

k1 = y1 = 1.066869388 
f(t1,k1) = 2.813524695 
k2 = y1 + 2 / 3hf(t1,k1) = 1.113761467 

y2 = y1 + h(1 / 4 f(t1,k1) + 3 / 4 f(t1 + 2 / 3h,k2)) = 1.141332181 

t3 = 1.075 

k1 = y2 = 1.141332181 
f(t2,k1) = 3.183536647 
k2 = y2 + 2 / 3hf(t2,k1) = 1.194391125 

y3 = y2 + h(1 / 4 f(t2,k1) + 3 / 4 f(t2 + 2 / 3h,k2)) = 1.227417567 

t4 = 1.1 

k1 = y3 = 1.227417567 
f(t3,k1) = 3.796866512 
k2 = y3 + 2 / 3hf(t3,k1) = 1.290698676 

y4 = y3 + h(1 / 4 f(t3,k1) + 3 / 4 f(t3 + 2 / 3h,k2)) = 1.335079087 

The numerical solutions correspond to the underlined values. Note that f(ti,k1) has been calculated to avoid recalculation in the yis.
The adaptive methods are designed to produce an estimate of the local truncation error of a single RungeKutta step. This is done by having two methods in the tableau, one with order p and one with order p − 1.
The lowerorder step is given by
where the ki are the same as for the higher order method. Then the error is
which is O(hp). The Butcher Tableau for this kind of method is extended to give the values of :
0 

c2 
a21 

c3 
a31 
a32 

cs 
as1 
as2 
as,s − 1 

b1 
b2 
bs − 1 
bs 

The RungeKuttaFehlberg method has two methods of orders 5 and 4. Its extended Butcher Tableau is:
0 

1/4 
1/4 

3/8 
3/32 
9/32 

12/13 
1932/2197 
−7200/2197 
7296/2197 

1 
439/216 
−8 
3680/513 
845/4104 

1/2 
−8/27 
2 
−3544/2565 
1859/4104 
−11/40 

16/135 
0 
6656/12825 
28561/56430 
−9/50 
2/55 

25/216 
0 
1408/2565 
2197/4104 
−1/5 
0 

However, the simplest adaptive RungeKutta method involves combining the Heun method, which is order 2, with the Euler method, which is order 1. Its extended Butcher Tableau is:
0 

1 
1 

1/2 
1/2 

1 
0 

The error estimate is used to control the stepsize.
Other adaptive RungeKutta methods are the BogackiShampine method (orders 3 and 2), the CashKarp method and the DormandPrince method (both with orders 5 and 4).
The implicit methods are more general than the explicit ones. The distinction shows up in the Butcher Tableau: for an implicit method, the coefficient matrix aij is not necessarily lower triangular:
The approximate solution to the initial value problem reflects the greater number of coefficients:
Due to the fullness of the matrix aij, the evaluation of each ki is now considerably involved and dependent on the specific function f(t,y). Despite the difficulties, implicit methods are of great importance due to their high (possibly unconditional) stability, which is especially important in the solution of partial differential equations. The simplest example of an implicit RungeKutta method is the backward Euler method:
The Butcher Tableau for this is simply:
It can be difficult to make sense of even this simple implicit method, as seen from the expression for k1:
In this case, the awkward expression above can be simplified by noting that
so that
from which
follows. Though simpler then the “raw” representation before manipulation, this is an implicit relation so that the actual solution is problem dependent. Multistep implicit methods have been used with success by some researchers. The combination of stability, higher order accuracy with fewer steps, and stepping that depends only on the previous value makes them attractive; however the complicated problemspecific implementation and the fact that ki must often be approximated iteratively means that they are not common.
J. C. Butcher, Numerical methods for ordinary differential equations, ISBN 0471967580
George E. Forsythe, Michael A. Malcolm, and Cleve B. Moler. Computer Methods for Mathematical Computations. Englewood Cliffs, NJ: PrenticeHall, 1977. (See Chapter 6.)
Ernst Hairer, Syvert Paul Nørsett, and Gerhard Wanner. Solving ordinary differential equations I: Nonstiff problems, second edition. Berlin: Springer Verlag, 1993. ISBN 3540566708.
William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T. Vetterling. Numerical Recipes in C. Cambridge, UK: Cambridge University Press, 1988. (See Sections 16.1 and 16.2.)
Kaw, Autar; Kalu, Egwu (2008), Numerical Methods with Applications (1st ed.), www.autarkaw.com.
Kendall E. Atkinson. An Introduction to Numerical Analysis. John Wiley & Sons – 1989
F. Cellier, E. Kofman. Continuous System Simulation. Springer Verlag, 2006. ISBN 0387261028.
RungeKutta
RungeKutta 4th Order Method
Runge Kutta Method for O.D.E.’s

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Higher Order Taylor Methods
Marcelo Julio Alvisio & Lisa Marie Danz
May 16, 2007
Introduction
Differential equations are one of the building blocks in science or engineering. Scientists aim to obtain numerical solutions to differential equations whenever explicit solutions do not exist or when they are too hard to find. These numerical solutions are approximated though a variety of methods, some of which we set out to explore in this project.
We require two conditions when computing differential equations numerically. First, we require that the solution is continuous with initial value. Otherwise, numerical error introduced in the representation of the number in computer systems would produce results very far from the actual solution. Second, we require that the solution changes continuously with respect to the differential equation itself. Otherwise, we cannot expect the method that approximates the differential equation to give accurate results.
The most common methods for computing differential equations numerically include Euler’s method, Higher Order Taylor method and RungeKutta methods. In this project, we concentrate on the “Higher Order Taylor Method.” This method employs the Taylor polynomial of the solution to the equation. It approximates the zeroth order term by using the previous step’s value (which is the initial condition for the first step), and the subsequent terms of the Taylor expansion by using the differential equation. We call it Higher Order Taylor Method, the “lower” order method being Euler’s Method.
Under certain conditions, the Higher Order Taylor Method limits the error to O(hn), where n is the order used. We will present several examples to test this idea. We will look into two main parameters as a measure of the effectiveness of the method, namely accuracy and efficiency.
Theory of the Higher Order Taylor Method
Definition 2.1 Consider the differential equation given by y0(t)= f(t,y), y(a)=
c. Then for b>a, the nth order Taylor approximation to y(b) with K steps is given by yK, where {yi} is defined recursively as:
t0 = a
y0 = y(a)= c
ti+1 = ti + h
h2 ∂f hn ∂n−1f
yi+1 = yi + hf(ti,yi)+ (ti,yi)+ … +(ti,yi)
2 ∂t n! ∂tn−1
with h =(b − a)/K.
It makes sense to formulate such a definition in view of the Taylor series expansion that is used when y(t) is known explicitly. All we have done is use f(t,y) for y0(t), ft(t,y) for y00(t), and so forth. The next task is to estimate the error that this approximation introduces.
We know by Taylor’s Theorem that, for any solution that admits a Taylor expansion at the point ti, we have h2 hn h(n+1) y(ti+1)= y(ti)+ hy0(ti)+ y00(ti)+ … + y(n)(ti)+ y(n+1)(σ)
2 n!(n + 1)!
where σ is between ti and ti+1
Using y0 = f(t,y), this translates to
h2 ∂f hn ∂(n−1)fh(n+1) ∂(n)f y(ti+1)= y(ti)+hf(ti,yi)+ (ti,yi)+…+(ti,yi)+ (σ,y(σ))
2 ∂t n! ∂t(n−1) (n + 1)! ∂t(n)
Therefore, the local error, that is to say, the error introduced at each step if the values calculated previously were exact, is given by:
1 ∂(n)f
Ei =(hn+1)(σ,y(σ))
(n + 1)! ∂tn which means that
1 ∂(n)f
max (hn+1)(σ,y(σ))
Ei ≤ σ∈[a,b] (n + 1)! ∂tn 23
We can say Ei = O(hn+1). Now, since the number of steps from a to b is proportional to 1/h, we multiply the error per step by the number of steps to find a total error
E = O(hn).
In Practice: Examples
We will consider differential equations that we can solve explicitly to obtain an equation for y(t) such that y0(t)= f(t,y). This way, we can calculate the actual error by subtracting the exact value for y(b) from the value that the Higher Order Taylor method predicts for it. To approximate values in the following examples, the derivatives of f(t,y) were computed by hand. MATLAB then performed the iteration and arrived at the approximation.
Notice that the definitions given in the previous section could also have been adapted for varying step size h. However, for ease of computation we have kept the step size constant. In our computations, we have chosen step size of (b − a)/2k, which resulted in K =2k evenly spaced points in the interval.
Example 3.1 We consider the differential equation
1+ t
y0(t)= f(t,y)=
1+ y
with initial condition y(1) = 2. It is clear that y(t)= √t2 +2t +6 − 1 solves this equation.
Thus we calculate the error for y(2) by subtracting the approximation of y(2) from y(2), which is the exact value. Recall that we are using h =2−k because (b − a)=1. The following table displays the errors calculated. k = 1 
k = 2 
k = 3 
k = 4 

order = 1 
.0333 
.0158 
.0077 
.0038 

order = 2 
−.0038 
−.0009 
−.0002 
−.0001 

order = 3 
.0003269 
.0000383 
.0000046 
.0000006 
The Taylor methods in the preceding section have the desirable feature that the F.G.E. is of order O(hN ), and N can be chosen large so that this error is small. However, the shortcomings of the Taylor methods are the a priori determination of N and the computation of the higher derivatives, which can be very complicated. Each RungeKutta method is derived from an appropriate Taylor method in such a way that the F.G.E. is of order O(hN ). A tradeoff is made to perform several function evaluations at each step and eliminate the necessity to compute the higher derivatives. These methods can be constructed for any order N. The RungeKutta method of order N = 4 is most popular.
It is a good choice for common purposes because it is quite accurate, stable, and easy to program. Most authorities proclaim that it is not necessary to go to a higherorder method because the increased accuracy is offset by additional computational effort. If more accuracy is required, then either a smaller step size or an adaptive method should be used.
The fourthorder RungeKutta method (RK4) simulates the accuracy of the Taylor series method of order N = 4. The method is based on computing yk+1 as follows:
(1) yk+1 = yk + w1k1 + w2k2 + w3k3 + w4k4,
where k1, k2, k3, and k4 have the form
(2)
k1 = h f (tk , yk ),
k2 = h f (tk + a1h, yk + b1k1),
k3 = h f (tk + a2h, yk + b2k1 + b3k2),
k4 = h f (tk + a3h, yk + b4k1 + b5k2 + b6k3).
By matching coefficients with those of the Taylor series method of order N = 4 so that the local truncation error is of order O(h5), Runge and Kutta were able to obtain the 490 CHAP. 9 SOLUTION OF DIFFERENTIAL EQUATIONS
following system of equations:
(3)
b1 = a1,
b2 + b3 = a2,
b4 + b5 + b6 = a3,
w1 + w2 + w3 + w4 = 1,
w2a1 + w3a2 + w4a3 = 1
2,
w2a2
1
+ w3a2
2
+ w4a2
3
= 1
3
,
w2a3
1
+ w3a3
2
+ w4a3
3
= 1
4
,
w3a1b3 + w4(a1b5 + a2b6) = 1
6
,
w3a1a2b3 + w4a3(a1b5 + a2b6) = 1
8
,
w3a2
1b3 + w4(a2
1b5 + a2
2b6) = 1
12
, w4a1b3b6 = 1
24
The system involves 11 equations in 13 unknowns. Two additional conditions must be
supplied to solve the system. The most useful choice is
(4) a1 = 1
2 and b2 = 0.
Then the solution for the remaining variables is
(5)
a2 = 1
2
, a3 = 1, b1 = 1
2
, b3 = 1
2
, b4 = 0, b5 = 0, b6 = 1,
w1 = 1
6
, w2 = 1
3
, w3 = 1
3
, w4 = 1
6
The values in (4) and (5) are substituted into (2) and (1) to obtain the formula for the standard RungeKutta method of order N = 4, which is stated as follows. Start with the initial point (t0, y0) and generate the sequence of approximations using
(6) yk+1 = yk + h( f1 + 2 f2 + 2 f3 + f4)
6
,
SEC. 9.5 RUNGEKUTTA METHODS 491
where
(7)
f1 = f (tk , yk ),
f2 = f
tk + h
2
, yk + h
2
f1
,
f3 = f
tk + h
2
, yk + h
2
f2
,
f4 = f (tk + h, yk + h f3).
Discussion about the Method
The complete development of the equations in (7) is beyond the scope of this book and can be found in advanced texts, but we can get some insights. Consider the graph of the solution curve y = y(t) over the first subinterval [t0, t1]. The function values in (7) are approximations for slopes to this curve. Here f1 is the slope at the left, f2 and f3 are two estimates for the slope in the middle, and f4 is the slope at the right (a)). The next point (t1, y1) is obtained by integrating the slope function
(8) y(t1) − y(t0) =
_ t1
t0
f (t, y(t)) dt.
If Simpson’s rule is applied with step size h/2, the approximation to the integral
in (8) is
(9)
_ t1
t0
f (t, y(t)) dt ≈ h
6
( f (t0, y(t0)) + 4 f (t1/2, y(t1/2)) + f (t1, y(t1))),
where t1/2 is the midpoint of the interval. Three function values are needed; hence we
make the obvious choice f (t0, y (t0)) = f1 and f (t1, y(t1)) ≈ f4. For the value in the
middle we chose the average of f2 and f3:
f (t1/2, y(t1/2)) ≈ f2 + f3
2
.
These values are substituted into (9), which is used in equation (8) to get y1:
(10) y1 = y0 + h
6
f1 + 4( f2 + f3)
2
+ f4
. When this formula is simplified, it is seen to be equation (6) with k = 0. The graph for the integral in (9) is shown in Figure 9.9(b).
492 CHAP. 9 SOLUTION OF DIFFERENTIAL EQUATIONS
y
t
m1 = f1
m2 = f3
m3 = f4
m4 = f4
(t0, y0)
y = y(t) (t1, y(t1))
t0 t1/2 t1
(a) Predicted slopes mj to the
solution curve y = y(t)
z
t
(t0, f1)
(t1/2, f2)
(t1/2, f3)
(t1, f4)
t0 t1/2 t1
(b) Integral approximation:
h
6
y(t1) − y0 = ( f1 + 2f2 + 2f3 + f4)
Figure 9.9 The graphs y = y(t) and z = f (t, y(t)) in the discussion of the RungeKutta
method of order N = 4.
Step Size versus Error
The error term for Simpson’s rule with step size h/2 is
(11) −y(4)(c1)
h5
2880
.
If the only error at each step is that given in (11), after M steps the accumulated error for the RK4 method would be
(12) −
_M
k=1
y(4)(ck)
h5
2880
≈ b − a
5760
y(4)(c)h4 ≈ O(h4).
The next theorem states the relationship between F.G.E. and step size. It is used to give us an idea of how much computing effort must be done when using the RK4 method.
Theorem 9.7 (Precision of the RungeKutta Method). Assume that y(t) is the solution to the I.V.P. If y(t) ∈ C5[t0, b] and {(tk , yk)}M
k=0 is the sequence of approximations
generated by the RungeKutta method of order 4, then
(13)
ek = y(tk ) − yk = O(h4),
_k+1 = y(tk+1) − yk − hTN (tk , yk) = O(h5).
SEC. 9.5 RUNGEKUTTA METHODS 493
In particular, the F.G.E. at the end of the interval will satisfy
(14) E(y(b), h) = y(b) − yM = O(h4).
Examples 9.10 and 9.11 illustrate Theorem 9.7. If approximations are computed using the step sizes h and h/2, we should have
(15) E(y(b), h) ≈ Ch4
for the larger step size, and
(16) E
y(b),
h
2
≈ C
h4
16
= 1
16
Ch4 ≈ 1
16
E(y(b), h).
Hence the idea in Theorem 9.7 is that if the step size in the RK4 method is reduced by a factor of 12
we can expect that the overall F.G.E. will be reduced by a factor of 1.
Example 9.10. Use the RK4 method to solve the I.V.P. y_ = (t − y)/2 on [0, 3] with
y(0) = 1. Compare solutions for h = 1, 12
, 14
, and 18
.
Table 9.8 gives the solution values at selected abscissas. For the step size h = 0.25, a sample calculation is
f1 = 0.0 − 1.0
2
= −0.5,
f2 = 0.125 − (1 + 0.25(0.5)(−0.5))
2
= −0.40625,
f3 = 0.125 − (1 + 0.25(0.5)(−0.40625))
2
= −0.4121094,
f4 = 0.25 − (1 + 0.25(−0.4121094))
2
= −0.3234863,
y1 = 1.0 + 0.25
−0.5 + 2(−0.40625) + 2(−0.4121094) − 0.3234863
6
= 0.8974915. _
Example 9.11. Compare the F.G.E. when the RK4 method is used to solve y_ = (t−y)/2
over [0, 3] with y(0) = 1 using step sizes 1, 12
, 14
, and 18
Table 9.9 gives the F.G.E. for the various step sizes and shows that the error in the approximation to y(3) decreases by about 1
16 when the step size is reduced by a factor
of 1/2.
E(y(3), h) = y(3) − yM = O(h4) ≈ Ch4 where C = −0.000614. _
A comparison of Examples 9.10 and 9.11 and Examples 9.8 and 9.9 shows what is
meant by the statement “The RK4 method simulates the Taylor series method of order
N = 4.” For these examples, the two methods generate identical solution sets {(tk , yk)}
494 CHAP. 9 SOLUTION OF DIFFERENTIAL EQUATIONS
Table 9.8 Comparison of the RK4 Solutions with Different Step Sizes for y_ = (t − y)/2
over [0, 3] with y(0) = 1
yk
tk h = 1 h = 12
h = 14
h = 18
y(tk ) Exact
0 1.0 1.0 1.0 1.0 1.0
0.125 0.9432392 0.9432392
0.25 0.8974915 0.8974908 0.8974917
0.375 0.8620874 0.8620874
0.50 0.8364258 0.8364037 0.8364024 0.8364023
0.75 0.8118696 0.8118679 0.8118678
1.00 0.8203125 0.8196285 0.8195940 0.8195921 0.8195920
1.50 0.9171423 0.9171021 0.9170998 0.9170997
2.00 1.1045125 1.1036826 1.1036408 1.1036385 1.1036383
2.50 1.3595575 1.3595168 1.3595145 1.3595144
3.00 1.6701860 1.6694308 1.6693928 1.6693906 1.6693905
Table 9.9 Relation between Step Size and F.G.E. for the RK4 Solutions to
y_ = (t − y)/2 over [0, 3] with y(0) = 1
Step
size, h
Number of
steps, M
Approximation
to y(3), yM
F.G.E.
Error at t = 3,
y(3) − yM
O(h4) ≈ Ch4
where
C = −0.000614
1 3 1.6701860 −0.0007955 −0.0006140
12
6 1.6694308 −0.0000403 −0.0000384
14
12 1.6693928 −0.0000023 −0.0000024
18
24 1.6693906 −0.0000001 −0.0000001
over the given interval. The advantage of the RK4 method is obvious; no formulas for the higher derivatives need to be computed nor do they have to be in the program.
It is not easy to determine the accuracy to which a RungeKutta solution has been computed. We could estimate the size of y(4)(c) and use formula (12). Another way is to repeat the algorithm using a smaller step size and compare results. A third way is to adaptively determine the step size, which is done in Program 9.5. In Section 9.6 we will see how to change the step size for a multistep method.
SEC. 9.5 RUNGEKUTTA METHODS 495
RungeKutta Methods of Order N = 2
The secondorder RungeKutta method (denoted RK2) simulates the accuracy of the Taylor series method of order 2. Although this method is not as good to use as the RK4 method, its proof is easier to understand and illustrates the principles involved.
To start, we write down the Taylor series formula for y(t + h):
(17) y(t + h) = y(t) + hy_
(t) + 1
2
h2 y__
(t) + CT h3 +· · · ,
where CT is a constant involving the third derivative of y(t) and the other terms in the series involve powers of h j for j > 3.
The derivatives y_
(t) and y__
(t) in equation (17) must be expressed in terms of
f (t, y) and its partial derivatives. Recall that
(18) y_
(t) = f (t, y).
The chain rule for differentiating a function of two variables can be used to differentiate (18) with respect to t, and the result is
y__
(t) = ft (t, y) + fy(t, y)y_
(t).
Using (18), this can be written
(19) y__
(t) = ft (t, y) + fy(t, y) f (t, y).
The derivatives (18) and (19) are substituted in (17) to give the Taylor expression for y(t + h):
y(t + h) = y(t) + h f (t, y) + 1
2
h2 ft (t, y)
+ 1
2
h2 fy(t, y) f (t, y) + CT h3 +· · · .
(20)
Now consider the RungeKutta method of order N = 2, which uses a linear combination of two function values to express y(t + h):
(21) y(t + h) = y(t) + Ah f0 + Bhf1,
where
(22)
f0 = f (t, y),
f1 = f (t + Ph, y + Qhf0).
Next the Taylor polynomial approximation for a function of two independent variables is used to expand f (t, y) (see the Exercises). This gives the following representation
for f1:
(23) f1 = f (t, y) + Phft (t, y) + Qhfy(t, y) f (t, y) + CPh2 +· · · ,
496 CHAP. 9 SOLUTION OF DIFFERENTIAL EQUATIONS
where CP involves the secondorder partial derivatives of f (t, y). Then (23) is used in (21) to get the RK2 expression for y(t + h):
y(t + h) = y(t) + (A + B)h f (t, y) + BPh2 ft (t, y)
+ BQh2 fy(t, y) f (t, y) + BCPh3 +· · · .
(24)
A comparison of similar terms in equations (20) and (24) will produce the following conclusions:
h f (t, y) = (A + B)h f (t, y) implies that 1 = A + B,
1
2
h2 ft (t, y) = BPh2 ft (t, y) implies that
1
2
= BP,
1
2
h2 fy(t, y) f (t, y) = BQh2 fy(t, y) f (t, y) implies that
1
2
= BQ.
Hence, if we require that A, B, P, and Q satisfy the relations
(25) A + B = 1 BP = 1
2
BQ = 1
2
,
then the RK2 method in (24) will have the same order of accuracy as the Taylor’s method in (20).
Since there are only three equations in four unknowns, the system of equations (25) is underdetermined, and we are permitted to choose one of the coefficients. There are several special choices that have been studied in the literature; we mention two of them.
Case (i): Choose A = 12
. This choice leads to B = 12
, P = 1, and Q = 1. If
equation (21) is written with these parameters, the formula is
(26) y(t + h) = y(t) + h
2
( f (t, y) + f (t + h, y + h f (t, y))).
When this scheme is used to generate {(tk , yk)}, the result is Heun’s method.
Case (ii): Choose A = 0. This choice leads to B = 1, P = 12
, and Q = 12
. If
equation (21) is written with these parameters, the formula is
(27) y(t + h) = y(t) + h f
t + h
2
, y + h
2
f (t, y)
.
When this scheme is used to generate {(tk , yk)}, it is called the modified EulerCauchy method.
Numerical Methods Using Matlab, 4th Edition, 2004
John H. Mathews and Kurtis K. Fink
ISBN: 0130652482
PrenticeHall Inc.
Upper Saddle River, New Jersey, USA
https://vig.prenhall.com/
Deriving the RungeKutta Method
Deriving the midpoint method
The Taylor method is the gold standard for generating better numerical solutions to first order differential equations. A serious weakness in the Taylor method, however, is the need to compute a large number of partial derivatives and do other symbolic manipulation tasks.
For example, the second order Taylor method for the equation
y·( t) = f(t,y(t))
is
yi+1 = yi + h f(ti ,yi ) + h2
2
“©© ©‘
Ûf
Ût
( ti ,yi ) + f ( ti ,yi ) Ûf
Ûy
( ti ,yi )
”™™ ™’
Higher order formulas get even uglier.
The Midpoint method arises from an attempt to replace the second order Taylor method with a simpler “Eulerlike” formula
yi+1 = yi + h f(ti + ¨,yi + ‡)
We can solve for the best values for ¨ and ‡ by applying a first order Taylor expansion to the term f(ti + ¨,yi + ‡):
yi+1 = yi + h
“©© ©‘
f ( ti ,yi ) + Ûf
Ût
( ti ,yi ) ¨ + Ûf
Ûy
( ti ,yi ) ‡ + Û
2f
Ût Ûy
( ti ,yi ) ¨ ‡
”™™ ™’
The choices of ¨ and ‡ that make this look as close as possible to the second order Taylor formula above are
¨ = h2
‡ = h2
f(ti ,yi )
leading to the socalled midpoint rule:
1
yi+1 = yi + h f(ti + h2
,yi + h2
f(ti ,yi))
This formula has a simple interpretation. Essentially what we are doing here is driving an Euler estimate half way across the interval [ti , ti+1] and computing the slope
f(ti + h2
,yi + h2
f(ti ,yi))
at that midpoint. We then rewind back to the point ( ti ,yi ) and drive an Euler estimate all the way across the interval to ti+1 using this new midpoint slope in place of the old Euler slope.
The RungeKutta Method
The textbook points out that it is possible to derive similar methods by starting with more complex Eulerlike formulas with more free parameters and then trying to match those Eulerlike methods to higher order Taylor formulas. The RungeKutta method is essentially an attempt to match a more complex Eulerlike formula to a fourth order Taylor method.
The problem with this is that the Eulerlike formula needed to match all the complexity of the fourth order Taylor method formula is quite complex. The textbook states in exercise 31 at the end of section 5.4 that the formula required is
yi+1 = yi + h6
f(ti ,yi ) + h3
f(ti + ¨1 h,yi + ç1 h f(ti ,yi)) + h3
f (ti + ¨2 h,yi + ç2 h f (ti + ›2 h, yi + ›3 h f ( ti ,yi ))) + h6
f (ti + ¨3 h, yi + ç3 h f (ti + ›4 h, yi + ›5 h f ( ti + ›6 h,yi+ ›7 h f ( ti ,yi))))
It is very messy to do so, but this form can expanded out and matched against the Taylor formula of order four. This allows us to solve for all the unknown coefficients. A somewhat cleaner alternative derivation is based on the following argument. Another way to solve for yi+1 is to compute this integral
!t i+1
t i
y·( t) dt = y(ti+1) – y(ti ) = yi+1 – yi
We can imagine beginning to compute the integral by noting that y·( t) = f(t,y(t))
!t i+1
t i
y·( t) dt = !t i+1
t i
f ( t,y( t)) dt
2
Unfortunately, we can not do the integral on the right hand side exactly, because we don’t know what y(t) is. That is, after all, the unknown we are trying to solve for. Even though we can’t compute the integral on the right exactly, we can estimate it. For example, applying Simpson’s rule to the integral produces the estimate
!t i+1
t i
f ( t,y( t)) dt ” h3
“©© ©‘f ( ti ,y( ti))+4 f
“©© ©‘
ti+ti+1
2
,y
“©© ©‘
ti+ti+1
2
”™™ ™’
”™™ ™’ + f ( ti+1,y( ti+1))
”™™ ™’
The RungeKutta method takes this estimate as a starting point. The thing we need to do to make this estimate work is to find a way to estimate the unknown terms
y((ti + ti+1) /2) and y(ti+1) .
The first step is to rewrite the estimate as
h3
“©© ©‘f ( ti ,y( ti))+2 f
“©© ©‘
ti+ti+1
2
,y
“©© ©‘
ti+ti+1
2
”™™ ™’
”™™ ™’ +2 f
“©© ©‘
ti+ti+1
2
,y
“©© ©‘
ti+ti+1
2
”™™ ™’
”™™ ™’+ f ( ti+1,y( ti+1))
”™™ ™’
We write the middle term twice because we are going to develop two different estimates for y((ti + ti+1) /2). The thinking is that the mistakes we make in developing those two interior estimates may partly cancel each other out.
Here is how we will develop our estimates.
1. y(ti ) is just yi .
We estimate the first y((ti + ti+1) /2) by driving the original Euler slope
k1 = f(ti ,yi ) halfway across the interval:
2.
k1 = f(ti ,y(ti))
y
“©© ©‘
ti + ti+1
2
”™™ ™’ ” yi + h/2 k1
As in the midpoint rule, we compute a second slope at that midpoint we just estimated. We then rewind to the start and drive that slope halfway across the interval again.
3.
k2 = f(ti + h/2,yi + h/2 k1 )
y
“©© ©‘
ti + ti+1
2
”™™ ™’ ” yi + h/2 k2
We use the second estimated midpoint to compute another slope and then drive that slope all the way across the interval.
4.
3 We use the second estimated midpoint to compute another slope and then drive that slope all the way across the interval.
4. k3 = f(ti + h/2,yi + h/2 k2 )
y(ti+1) = yi + h k3
k4 = f(ti + h,yi + h k3 )
Substituting all of these estimates into the Simpson’s rule formula above gives yi+1 – yi = !t i+1
t i
f ( t,y( t)) dt “
h3
“©© ©‘f ( ti ,y( ti))+ 2 f
“©© ©‘
ti+ti+1
2
,y
“©© ©‘
ti+ti+1
2
”™™ ™’
”™™ ™’+2 f
“©© ©‘
ti+ti+1
2
,y
“©© ©‘
ti+ti+1
2
”™™ ™’
”™™ ™’+ f ( ti+1,y( ti+1))
”™™ ™’
or
yi+1 = yi + h3
(k1 + 2 k2 + 2 k3 + k4 )
k1 = f(ti ,yi )
k2 = f(ti + h/2,yi + h/2 k1 )
k3 = f(ti + h/2,yi + h/2 k2 )
k4 = f(ti + h,yi + h k3 )
yi+1 = yi + h3(k1 + 2 k2 + 2 k3 + k4 )
4 Taylor Series Methods: To derive these methods we start with a Taylor Expansion:
y(t+_t) _ y(t) + _ty0(t) +
1
2
_t2y00(t) + …+
1
r!
y(r)(t)_tr.
Let’s say we want to truncate this at the second derivative and base a method on that.
The scheme is, then:
yn+1 = yn + fn_t +
f0
tn
2
_t2.
The Taylor series method can be written as
yn+1 = yn +_tF (tn, yn,_t)
where F = f + 1
2_tf0. If we take the LTE for this scheme, we get (as expected) LTE(t) =
y(tn +_t) − y(tn)
_t
− f(tn, y(tn)) −
1
2
_tf0(tn, y(tn)) = O(_t2).
Of course, we designed this method to give us this order, so it shouldn’t be a surprise!
So the LTE is reasonable, but what about the global error? Just as in the Euler Forward case, we can show that the global error is of the same order as the LTE. How do we do this?
We have two facts,
y(tn+1) = y(tn) + _tF (tn, y(tn),_t),
and
yn+1 = yn +_tF (tn, yn,_t)
where F = f + 1
2_tf0. Now we subtract these two
y(tn+1) − yn+1 = y(tn) − yn +_t (F(tn, y(tn)) − F(tn, yn)) + _tLTE
_ y(tn) − yn+_t F(tn, y(tn)) − F(tn, yn)+_tLTE .
Now, if F is Lipschitz continuous, we can say
en+1 _ (1 + _tL)en+_tLTE.
Of course, this is the same proof as for Euler’s method, except that now we are looking at F, not f, and the LTE is of higher order. We can do this no matter which Taylor series method we use, how many terms we go forward before we truncate.
advantages a) One step, explicit
b) can be high order
c) easy to show that global error is the same order as LTE
disadvantages Needs the explicit form of derivatives of f.
4 RungeKutta Methods To avoid the disadvantage of the Taylor series method, we can use RungeKutta methods. These are still one step methods, but they depend on estimates of the solution at different points. They are written out so that they don’t look messy:
Second Order RungeKutta Methods:
k1 = _tf(ti, yi)
k2 = _tf(ti + __t, yi + _k1)
yi+1 = yi + ak1 + bk2
let’s see how we can chose the parameters a,b, _, _ so that this method has the highest order LTE possible. Take the Taylor expansions to express the LTE:
k1(t) = _tf(t, y(t))
k2(t) = _tf(t + __t, y + _k1(t)
= _t
_
f(t, y(t) + ft(t, y(t))__t+ fy(t, y(t))_k1(t) + O(_t2)
_
LTE(t) =
y(t+_t) − y(t)
_t
−
a
_t
f(t, y(t))_t −
b
_t
(ft(t, y(t))__t+ fy(t, y(t)_k1(t)
+ f(t, y(t))_t + O(_t2)
=
y(t+_t) − y(t)
_t
− af(t, y(t)) − bf(t, y(t))− bft(t, y(t))_
− bfy(t, y(t)_f(t, y(t))+ O(_t2)
= y0(t) +
1
2
_ty00(t) − (a + b)f(t, y(t)) − _t(b_ft(t, y(t))+ b_f(t, y(t))fy(t, y(t)) + O(_t2)
= (1− a − b)f + (
1
2
− b_)_tft + (
1
2
− b_)_tfyf + O(_t2)
So we want a = 1− b, _ = _ = 1
2b .
Fourth Order RungeKutta Methods:
k1 = _tf(ti, yi) (1.3)
k2 = _tf(ti +
1
2
_t, yi +
1
2
k1) (1.4)
k3 = _tf(ti +
1
2
_t, yi +
1
2
k2) (1.5)
k4 = _tf(ti+_t, yi + k3) (1.6)
yi+1 = yi +
1
6
(k1 + k2 + k3 + k4) (1.7)
The second order method requires 2 evaluations of f at every timestep, the fourth order method requires 4 evaluations of f at every timestep. In general: For an rth order Runge Kutta method we need S(r) evaluations of f for each timestep, where
S(r) =
8><
>:
r for r _ 4
r + 1 for r = 5 and r = 6
_ r + 2 for r _ 7
5
Practically speaking, people stop at r = 5.
Advantages of RungeKutta Methods
1. One step method – global error is of the same order as local error.
2. Don’t need to know derivatives of f.
3. Easy for ”Automatic Error Control”.
Automatic Error Control Uniform grid spacing – in this case, time steps – are good for some cases but not always. Sometimes we deal with problems where varying the gridsize makes sense. How do you know when to change the stepsize? If we have an rth order scheme and and r + 1th order scheme, we can take the difference between these two to be the error in the scheme, and make the stepsize smaller if we prefer a smaller error, or larger if we can tolerate a larger error.
For Automatic error control yo are computing a ”useless” (r+1)th order shceme . . .
what a waste! But with Runge Kutta we can take a fifth order method and a fourth order method, using the same ks. only a little extra work at each step.
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