Interpolation, approximation and differential equations solvers
Interpolation, approximation and differential equations solvers
Contents
Problem 1
1.1 Problem definition
1.2 Solution of the problem
1.2.1 Linear interpolation
1.2.2 Method of least squares
interpolation
1.2.3 Lagrange interpolating polynomial
1.2.4 Cubic spline interpolation
1.3 Results and discussion
1.3.1 Lagrange polynomial
Problem 2
2.1 Problem definition
2.2 Problem solution
2.2.1 Rectangular method
2.2.2 Trapezoidal rule
2.2.3 Simpson's rule
2.2.4 GaussLegendre method and
GaussChebyshev method
Problem 3
3.1 Problem definition
3.2 Problem solution
Problem 4
4.1 Problem definition
4.2 Problem solution
References
1.1 Problem definition
For the
following data set, please discuss the possibility of obtaining a reasonable
interpolated value at , , and via at least 4 different interpolation formulas
you are have learned in this semester.
1.2 Solution of the problem
Interpolation
is a method of constructing new data points within the range of a discrete set
of known data points.
In engineering
and science one often has a number of data points, as obtained by sampling or
experimentation, and tries to construct a function which closely fits those
data points. This is called curve fitting or regression analysis. Interpolation
is a specific case of curve fitting, in which the function must go exactly
through the data points.
First we have
to plot data points, such plot provides better picture for analysis than data
arrays
Following four
interpolation methods will be discussed in order to solve the problem:
·
Linear
interpolation
·
Method
of least squares interpolation
·
Lagrange
interpolating polynomial
Fig 1. Initial
data points
·
Cubic
spline interpolation
1.2.1
Linear interpolation
One of the
simplest methods is linear interpolation (sometimes known as lerp). Generally,
linear interpolation tales two data points, say and , and the interpolant is given by:
at the point
Linear
interpolation is quick and easy, but it is not very precise/ Another
disadvantage is that the interpolant is not differentiable at the point .
The method of
least squares is an alternative to interpolation for fitting a function to a
set of points. Unlike interpolation, it does not require the fitted function to
intersect each point. The method of least squares is probably best known for
its use in statistical regression, but it is used in many contexts unrelated to
statistics.
Fig 2. Plot of
the data with linear interpolation superimposed
Generally, if
we have data points, there is exactly one polynomial
of degree at most going through all the data points.
The interpolation error is proportional to the distance between the data points
to the power n. Furthermore, the interpolant is a polynomial and thus
infinitely differentiable. So, we see that polynomial interpolation solves all
the problems of linear interpolation.
However,
polynomial interpolation also has some disadvantages. Calculating the
interpolating polynomial is computationaly expensive compared to linear
interpolation. Furthermore, polynomial interpolation may not be so exact after
all, especially at the end points. These disadvantages can be avoided by using
spline interpolation.
Example of
construction of polynomial by least square method
Data is given
by the table:
Polynomial is
given by the model:
In order to
find the optimal parameters the following
substitution is being executed:
, ,
…,
Then:
The error
function:
It is
necessary to find parameters , which provide minimums
to function :
It should be
noted that the matrix must be nonsingular matrix.
For the given
data points matrix become singular, and it makes
impossible to construct polynomial with order,
where  number of data points, so we will use polynomial
Fig 3. Plot of
the data with polynomial interpolation superimposed
Because the
polynomial is forced to intercept every point, it weaves up and down.
1.2.3
Lagrange interpolating polynomial
The Lagrange
interpolating polynomial is the polynomial of degree
that passes through the points , , …, and is
given by:
,
Where
Written
explicitly
When
constructing interpolating polynomials, there is a tradeoff between having a
better fit and having a smooth wellbehaved fitting function. The more data
points that are used in the interpolation, the higher the degree of the
resulting polynomial, and therefore the greater oscillation it will exhibit
between the data points. Therefore, a highdegree interpolation may be a poor
predictor of the function between points, although the accuracy at the data
points will be "perfect."
Fig 4. Plot of
the data with Lagrange interpolating polynomial interpolation superimposed
One can see,
that Lagrange polynomial has a lot of oscillations due to the high order if
polynomial.
1.2.4 Cubic
spline interpolation
Remember that
linear interpolation uses a linear function for each of intervals . Spline interpolation uses lowdegree
polynomials in each of the intervals, and chooses the polynomial pieces such
that they fit smoothly together. The resulting function is called a spline. For
instance, the natural cubic spline is piecewise cubic and twice continuously
differentiable. Furthermore, its second derivative is zero at the end points.
Like
polynomial interpolation, spline interpolation incurs a smaller error than
linear interpolation and the interpolant is smoother. However, the interpolant
is easier to evaluate than the highdegree polynomials used in polynomial
interpolation. It also does not suffer from Runge's phenomenon.
Fig 5. Plot of
the data with Lagrange interpolating polynomial interpolation superimposed
It should be
noted that cubic spline curve looks like metal ruler fixed in the nodal points,
one can see that such interpolation method could not be used for modeling
sudden data points jumps.
1.3 Results and discussion
The following
results were obtained by employing described interpolation methods for the
points ; ; :

Linear interpolation

Least squares interpolation


Cubic spline

Root mean square


0.148

0.209

0.015

0.14

0.146


0.678

0.664

0.612

0.641

0.649


1.569

1.649

1.479

1.562

1.566

Table 1.
Results of interpolation by different methods in the given points.
In order to determine
the best interpolation method for the current case should be constructed the
table of deviation between interpolation results and root mean square, if
number of interpolations methods increases then value of RMS become closer to
the true value.

Linear interpolation

Least squares interpolation

Lagrange polynomial

Cubic spline
















Average deviation from the RMS





Table 2. Table
of Average deviation between average deviation and interpolation results.
One can see
that cubic spline interpolation gives the best results among discussed methods,
but it should be noted that sometimes cubic spline gives wrong interpolation,
especially near the sudden function change. Also good interpolation results are
provided by Linear interpolation method, but actually this method gives average
values on each segment between values on it boundaries.
2.1 Problem definition
For the above
mentioned data set, if you are asked to give the integration of between two ends and ? Please discuss the possibility accuracies
of all the numerical integration formulas you have learned in this semester.
2.2 Problem solution
In numerical
analysis, numerical integration constitutes a broad family of algorithms for
calculating the numerical value of a definite integral.
There are
several reasons for carrying out numerical integration. The integrand may be known only at certain points, such as
obtained by sampling. Some embedded systems and other computer applications may
need numerical integration for this reason.
A formula for
the integrand may be known, but it may be difficult or impossible to find an
antiderivative which is an elementary function. An example of such an integrand
is , the antiderivative of which cannot be
written in elementary form.
It may be
possible to find an antiderivative symbolically, but it may be easier to
compute a numerical approximation than to compute the antiderivative. That may
be the case if the antiderivative is given as an infinite series or product, or
if its evaluation requires a special function which is not available.
The following
methods were described in this semester:
·
Rectangular
method
·
Trapezoidal
rule
·
Simpson's
rule
·
GaussLegendre
method
·
GaussChebyshev
method
2.2.1
Rectangular method
The most
straightforward way to approximate the area under a curve is to divide up the
interval along the xaxis between and into a number of smaller intervals, each of
the same length. For example, if we divide the interval into subintervals, then the width of each one will
be given by:
The
approximate area under the curve is then simply the sum of the areas of all the
rectangles formed by our subintervals:
The summary
approximation error for intervals with width is less than or equal to
Thus it is
impossible to calculate maximum of the derivative function, we can estimate
integration error like value:
2.2.2
Trapezoidal rule
Trapezoidal
rule is a way to approximately calculate the definite integral. The trapezium
rule works by approximating the region under the graph of the function by a trapezium and calculating its area. It
follows that
To calculate
this integral more accurately, one first splits the interval of integration into n smaller subintervals, and then
applies the trapezium rule on each of them. One obtains the composite trapezium
rule:
The summary
approximation error for intervals with width is less than or equal to:
2.2.3
Simpson's rule
Simpson's rule
is a method for numerical integration, the numerical approximation of definite
integrals. Specifically, it is the following approximation:
If the
interval of integration is in some sense
"small", then Simpson's rule will provide an adequate approximation
to the exact integral. By small, what we really mean is that the function being
integrated is relatively smooth over the interval . For such
a function, a smooth quadratic interpolant like the one used in Simpson's rule
will give good results.
However, it is
often the case that the function we are trying to integrate is not smooth over
the interval. Typically, this means that either the function is highly
oscillatory, or it lacks derivatives at certain points. In these cases,
Simpson's rule may give very poor results. One common way of handling this
problem is by breaking up the interval into a
number of small subintervals. Simpson's rule is then applied to each
subinterval, with the results being summed to produce an approximation for the
integral over the entire interval. This sort of approach is termed the
composite Simpson's rule.
Suppose that
the interval is split up in subintervals,
with n an even number. Then, the composite Simpson's rule is given by
The error
committed by the composite Simpson's rule is bounded (in absolute value) by
2.2.4
GaussLegendre method and GaussChebyshev method
Since function values are given in fixed points then just two points
GaussLegendre method can be applied. If is
continuous on , then
The GaussLegendre rule G2( f ) has degree of precision . If , then
,
where
It should be
noted that even in case of two points method we have to calculate values of the
function in related to , ,
this values could be evaluated by linear interpolation (because it is necessary
to avoid oscillations), so estimation of integration error become very
complicated process, but this error will be less or equal to trapezoidal rule.
Mechanism of
GaussChebyshev method is almost the same like described above, and integration
error will be almost the same, so there is no reason to use such methods for
the current problem.
3.1 Problem definition
It is well
known that the third order RungeKutta method is of the following form
,
Suppose that
you are asked to derived a new third order RungeKutta method in the following
from
,
Find determine
the unknowns , , and
so that your scheme is a third order
RungeKutta method.
3.2 Problem solution
Generally
RungeKutta method looks like:
,
where
coefficients could be calculated by the scheme:
The error
function:
Coefficients , , must
be found to satisfy conditions , consequently we can
suppose that for each order of RungeKutta scheme those coefficients are
determined uniquely, it means that there are no two different third order
schemes with different coefficients. Now it is necessary to prove statement.
For , :
;
;
; ;
;
; ;
Thus we have
system of equations:
Some of
coefficients are already predefined:
; ;
; ; ;
; ;
Obtained
results show that RungeKutta scheme for every order is unique.
Problem
4
4.1 Problem definition
Discuss the
stability problem of solving the ordinary equation , via the Euler explicit scheme , say . If , how to apply your stability restriction?
4.2 Problem solution
The Euler
method is 1st order accurate. Given scheme could be rewritten in form of:
If has a magnitude greater than one then will tend to grow with increasing and may eventually dominate over the
required solution. Hence the Euler method is stable only if or:
For the case mentioned above inequality looks like:
Last result
shows that integration step mast be less or equal to .
For the case , for the iteration method coefficient looks
like
As step is positive value of the function must be less then .
There are two ways to define the best value of step , the firs
one is to define maximum value of function on the
integration area, another way is to use different for each
value , where . So
integration step is strongly depends on value of .
1.
J. C.
Butcher, Numerical methods for ordinary differential equations, ISBN 0471967580
2.
George
E. Forsythe, Michael A. Malcolm, and Cleve B. Moler. Computer Methods for
Mathematical Computations. Englewood Cliffs, NJ: PrenticeHall, 1977. (See
Chapter 6.)
3.
Ernst
Hairer, Syvert Paul Nørsett, and Gerhard Wanner. Solving ordinary
differential equations I: Nonstiff problems, second edition. Berlin: Springer
Verlag, 1993. ISBN 3540566708.
4.
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.)
5.
Kendall
E. Atkinson. An Introduction to Numerical Analysis. John Wiley & Sons 
1989
6.
F.
Cellier, E. Kofman. Continuous System Simulation. Springer Verlag, 2006. ISBN
0387261028.
7.
Gaussian
Quadrature Rule of Integration  Notes, PPT, Matlab, Mathematica, Maple,
Mathcad at Holistic Numerical Methods Institute
8.
Burden,
Richard L.; J. Douglas Faires (2000). Numerical Analysis (7th Ed. ed.).
Brooks/Cole. ISBN 0534382169.
