test_approx
test_approx,
a MATLAB code which
provides sets of test data for approximation algorithms.
TEST_APPROX contains a number of vectors of data (X(1:N),Y(1:N))
for which no underlying functional relationship is given.
The task of interpolation software is to find, from some given
class of functions, the function G(X) which exactly matches the
known data values. That is, G(X(1:N)) = Y(1:N).
The task of approximation software is to find, from some given
class of functions, the function H(X) for which some approximation
error is minimized. There are many forms of error measurement.
For instance, the error might simply be the sum of the differences
of the function and the data at the data abscissas:
l1(X) = sum ( 1 <= I <= N ) abs ( H(X(I))  Y(I) )
or the square root of the sum of squares
l2(X) = sqrt ( sum ( 1 <= I <= N ) ( H(X(I))  Y(I) )^2 )
or the maximum pointwise error:
l_inf(X) = max ( abs ( H(X(I))  Y(I) ) )
In cases where a functional form is given, the error might be
measured in terms of the integral of the absolute value of the
difference over some interval:
L1(X,A,B) = integral ( A <= X <= B ) abs ( H(X)  F(X) ) dx
and so on.
The problems available include:

p01_data.png: DeBoor example, Mars position data

p02_data.png: DeBoor example, roughly linear data

p03_data.png: The pulse data, 0 0 0 0 0 1 0 0 0 0 0;

p04_data.png: The jump data, 0 0 0 0 0 1/2 1 1 1 1 1;

p05_data.png: DeBoor's Titanium Property data;

p06_data.png: The Sawtooth data;

p07_data.png: Concavity test data;

p08_data.png: Extrapolation test data;

p09_data.png: Sunspot data, 17001960;

p10_data.png: 100 samples of y=2+5x+10*N(0,1),
where N(0,1) is a random normal value with 0 mean and unit variance;
Licensing:
The computer code and data files described and made available on this web page
are distributed under
the GNU LGPL license.
Languages:
test_approx is available in
a C version and
a C++ version and
a FORTRAN90 version and
a MATLAB version.
Related Data and Programs:
bernstein_polynomial,
a MATLAB code which
evaluates the Bernstein polynomials,
useful for uniform approximation of functions;
chebyshev,
a MATLAB code which
computes the Chebyshev interpolant/approximant to a given function
over an interval.
lagrange_approx_1d,
a MATLAB code which
defines and evaluates the Lagrange polynomial p(x) of degree m
which approximates a set of nd data points (x(i),y(i)).
pwl_approx_1d,
a MATLAB code which
approximates a set of data using a piecewise linear function.
spline,
a MATLAB code which
includes many routines to construct and evaluate spline
interpolants and approximants.
test_approx,
a dataset directory which
contains sets of data (x,y) for which an approximating formula is desired.
test_approx_test
test_interp,
a MATLAB code which
defines a number of test problems for interpolation,
provided as a set of (x,y) data.
test_interp_1d,
a MATLAB code which
defines test problems for interpolation of data y(x),
depending on a 1D argument.
vandermonde_approx_1d,
a MATLAB code which
finds a polynomial approximant to a function of 1D data
by setting up and solving an overdetermined linear system for the
polynomial coefficients, involving the Vandermonde matrix.
Reference:

Samuel Conte, Carl deBoor,
Elementary Numerical Analysis,
Second Edition,
McGraw Hill, 1972,
ISBN: 070124464,
LC: QA297.C65.

Carl deBoor,
A Practical Guide to Splines,
Springer, 2001,
ISBN: 0387953663,
LC: QA1.A647.v27.

Max Waldmeier,
The SunspotActivity in the Years 16101960,
Shulthess, 1961,
LC: QB525.W34.
Source Code:

p00_dat.m,
returns the data vector for any problem.

p00_data_num.m,
returns the dimension of the data vector for any problem.

p00_data_plot.m,
plots the data.

p00_prob_num.m,
returns the number of test problems.

p00_story.m,
prints the "story" for any problem.

p00_title.m,
returns the title of any problem.

p01_dat.m,
returns the data vector for problem 1.

p01_data_num.m,
returns the dimension of the data vector for problem 1.

p01_story.m,
prints the "story" for problem 1.

p01_title.m,
returns the title of problem 1.

p02_dat.m,
returns the data vector for problem 2.

p02_data_num.m,
returns the dimension of the data vector for problem 2.

p02_story.m,
prints the "story" for problem 2.

p02_title.m,
returns the title of problem 2.

p03_dat.m,
returns the data vector for problem 3.

p03_data_num.m,
returns the dimension of the data vector for problem 3.

p03_story.m,
prints the "story" for problem 3.

p03_title.m,
returns the title of problem 3.

p04_dat.m,
returns the data vector for problem 4.

p04_data_num.m,
returns the dimension of the data vector for problem 4.

p04_story.m,
prints the "story" for problem 4.

p04_title.m,
returns the title of problem 4.

p05_dat.m,
returns the data vector for problem 5.

p05_data_num.m,
returns the dimension of the data vector for problem 5.

p05_story.m,
prints the "story" for problem 5.

p05_title.m,
returns the title of problem 5.

p06_dat.m,
returns the data vector for problem 6.

p06_data_num.m,
returns the dimension of the data vector for problem 6.

p06_story.m,
prints the "story" for problem 6.

p06_title.m,
returns the title of problem 6.

p07_dat.m,
returns the data vector for problem 7.

p07_data_num.m,
returns the dimension of the data vector for problem 7.

p07_story.m,
prints the "story" for problem 7.

p07_title.m,
returns the title of problem 7.

p08_dat.m,
returns the data vector for problem 8.

p08_data_num.m,
returns the dimension of the data vector for problem 8.

p08_story.m,
prints the "story" for problem 8.

p08_title.m,
returns the title of problem 8.

p09_dat.m,
returns the data vector for problem 9.

p09_data_num.m,
returns the dimension of the data vector for problem 9.

p09_story.m,
prints the "story" for problem 9.

p09_title.m,
returns the title of problem 9.

p10_dat.m,
returns the data vector for problem 10.

p10_data_num.m,
returns the dimension of the data vector for problem 10.

p10_story.m,
prints the "story" for problem 10.

p10_title.m,
returns the title of problem 10.

r8vec2_print.m,
prints an R8VEC2.

r8vec2_write.m,
writes an R8VEC2 to a file.
Last revised on 28 March 2019.