dlib/examples/integrate_function_adapt_simp_ex.cpp

// Copyright (C) 2013 Steve Taylor (steve98654@gmai.com)
// The contents of this file are in the public domain.  See
// LICENSE_FOR_EXAMPLE_PROGRAMS.txt

/*

    This example demonstrates the usage of the numerical quadrature function 
    integrate_function_adapt_simpson.  This function takes as input a single variable 
    function, the endpoints of a domain over which the function will be integrated, and a 
    tolerance parameter.  It outputs an approximation of the integral of this function 
    over the specified domain.  The algorithm is based on the adaptive Simpson method outlined in: 

    Numerical Integration method based on the adaptive Simpson method in
    Gander, W. and W. Gautschi, "Adaptive Quadrature – Revisited,"
    BIT, Vol. 40, 2000, pp. 84-101

*/

#include <iostream>
#include <stdint.h>
#include <dlib/matrix.h>
#include <dlib/numeric_constants.h>
#include <dlib/integrate_function_adapt_simpson.h>

using namespace std;
using namespace dlib;

// Here we define a class that consists of the set of functions that we 
// wish to integrate and comment in the domain of integration.

class int_fcts {
    
public:

    // x in [0,1]
    static double gg1(double x)
    {
        return pow(e,x);
    }   

    // x in [0,1]
    static double gg2(double x)
    {
        return x*x;
    }

    // x in [0, pi]
    static double gg3(double x)
    {
        return 1/(x*x + cos(x)*cos(x));
    }

    // x in [-pi, pi]
    static double gg4(double x)
    {
        return sin(x);
    }

    // x in [0,2]
    static double gg5(double x)
    {
        return 1/(1 + x*x);
    }
};

// Examples 
int main()
{
    // We first define a matrix m to which we will store the approximated values of the
    // integrals of our int_fcts functions.
    
    matrix<double,5,1> m;
    
    // Next we define a tolerance parameter.  Roughly speaking, a lower tolerance will
    // result in a more accurate approximation of the true integral.  However, there are 
    // instances where too small of a tolerance may yield a less accurate approximation
    // than a larger tolerance.  We recommend taking the tolerance to be in the
    // [1e-10, 1e-8] region.
    
    double tol = 1e-10;

    // Here we instantiate a class which contains the numerical quadrature method.
    adapt_simp ad;

    // Here we compute the integrals of the five functions defined above using the same 
    // tolerance level for each.

    m(0) = ad.integrate_function_adapt_simp(&int_fcts::gg1, 0.0, 1.0, tol);
    m(1) = ad.integrate_function_adapt_simp(&int_fcts::gg2, 0.0, 1.0, tol);
    m(2) = ad.integrate_function_adapt_simp(&int_fcts::gg3, 0.0, pi, tol);
    m(3) = ad.integrate_function_adapt_simp(&int_fcts::gg4, -pi, pi, tol);
    m(4) = ad.integrate_function_adapt_simp(&int_fcts::gg5, 0.0, 2.0, tol);

    // We finally print out the values of each of the approximated integrals to ten
    // significant digits.

    cout << "\nThe integral of exp(x) for x in [0,1] is "          << std::setprecision(10) <<  m(0)  << endl; 
    cout << "The integral of x^2 for in [0,1] is "                 << std::setprecision(10) <<  m(1)  << endl; 
    cout << "The integral of 1/(x^2 + cos(x)^2) for in [0,pi] is " << std::setprecision(10) <<  m(2)  << endl;
    cout << "The integral of sin(x) for in [-pi,pi] is "           << std::setprecision(10) <<  m(3)  << endl;
    cout << "The integral of 1/(1+x^2) for in [0,2] is "           << std::setprecision(10) <<  m(4)  << endl;
    cout << "" << endl;

    return 0;
}