test_approx

test_approx, an Octave code which provides sets of test data for approximation algorithms.

This code 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) )

l2(X) = sqrt ( sum ( 1 <= I <= N ) ( H(X(I)) - Y(I) )^2 )

l_inf(X) = max ( abs ( H(X(I)) - Y(I) ) )

L1(X,A,B) = integral ( A <= X <= B ) abs ( H(X) - F(X) ) dx

The problems available include:

1. p01_data.png: DeBoor example, Mars position data
2. p02_data.png: DeBoor example, roughly linear data
3. p03_data.png: The pulse data, 0 0 0 0 0 1 0 0 0 0 0;
4. p04_data.png: The jump data, 0 0 0 0 0 1/2 1 1 1 1 1;
5. p05_data.png: DeBoor's Titanium Property data;
6. p06_data.png: The Sawtooth data;
7. p07_data.png: Concavity test data;
8. p08_data.png: Extrapolation test data;
9. p09_data.png: Sunspot data, 1700-1960;
10. 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 information on this web page is distributed under the MIT license.

Languages:

test_approx is available in a C version and a C++ version and a Fortran77 version and a Fortran90 version and a MATLAB version and an Octave version and a Python version.

Related Data and Programs:

test_approx_test

bernstein_polynomial, an Octave code which evaluates the Bernstein polynomials, useful for uniform approximation of functions;

chebyshev, an Octave code which computes the Chebyshev interpolant/approximant to a given function over an interval.

lagrange_approx_1d, an Octave 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, an Octave code which approximates a set of data using a piecewise linear function.

spline, an Octave 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_interp, an Octave code which defines a number of test problems for interpolation, provided as a set of (x,y) data.

test_interp_1d, an Octave code which defines test problems for interpolation of data y(x), depending on a 1D argument.

vandermonde_approx_1d, an Octave 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:

1. Samuel Conte, Carl deBoor,
   Elementary Numerical Analysis,
   Second Edition,
   McGraw Hill, 1972,
   ISBN: 07-012446-4,
   LC: QA297.C65.
2. Carl deBoor,
   A Practical Guide to Splines,
   Springer, 2001,
   ISBN: 0387953663,
   LC: QA1.A647.v27.
3. Max Waldmeier,
   The Sunspot-Activity in the Years 1610-1960,
   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.