feat: Add Runge Kutta Method (#1286)

* Runge Kutta Method

* statically cast and initialised

* changes applied

* changes applied

* minor changes applied

* Proper Documentation updated

* Proper Documentation updated with suggestions

* minor changes

* added returns to rungeKutta function

* made a small commit

* updating DIRECTORY.md

* made a small change at line 99

* made a small change at line 99

* Changes made as suggested

* Changes updated

* espace reverted

* changes made in the documentation

* minor additions

* Update rungekutta.cpp

* Update change

* Changes removed

* Minor updates

* test functions added

* Test functions outside namespace

* non-static to static test function

* used vector

* changed function brief

* made a single test function

* made a single test function and made a slight change

* changed numerical method namespace description

* made changes in test function

* made slight changes as suggested

* Added a space

* Added the new suggestions

* updated in latex

Co-authored-by: Rudra Prasad Das <rudraiitism@gmail.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
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* [Qr Decompose](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_decompose.h) * [Qr Decompose](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_decompose.h)
* [Qr Decomposition](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_decomposition.cpp) * [Qr Decomposition](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_decomposition.cpp)
* [Qr Eigen Values](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_eigen_values.cpp) * [Qr Eigen Values](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/qr_eigen_values.cpp)
* [Rungekutta](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/rungekutta.cpp)
* [Successive Approximation](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/successive_approximation.cpp) * [Successive Approximation](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/numerical_methods/successive_approximation.cpp)
## Operations On Datastructures ## Operations On Datastructures

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/**
* @{
* \file
* \brief [Runge Kutta fourth order](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods) method implementation
*
* \author [Rudra Prasad Das](http://github.com/rudra697)
*
* \details
* It solves the unknown value of y
* for a given value of x
* only first order differential equations
* can be solved
* \example
* it solves \frac{\mathrm{d} y}{\mathrm{d} x}= \frac{\left ( x-y \right )}{2}
* given x for given initial
* conditions
* There can be many such equations
*/
#include <iostream> /// for io operations
#include <vector> /// for using the vector container
#include <cassert> /// asserting the test functions
/**
* @brief The change() function is used
* to return the updated iterative value corresponding
* to the given function
* @param x is the value corresponding to the x coordinate
* @param y is the value corresponding to the y coordinate
* @returns the computed function value at that call
*/
static double change(double x, double y)
{
return ((x - y)/2.0);
}
/**
* @namespace numerical_methods
* @brief Numerical Methods
*/
namespace numerical_methods {
/**
* @namespace runge_kutta
* @brief Functions for [Runge Kutta fourth order](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods) method
*/
namespace runge_kutta {
/**
* @brief the Runge Kutta method finds the value of integration of a function in the given limits.
* the lower limit of integration as the initial value and the upper limit is the given x
* @param init_x is the value of initial x and is updated after each call
* @param init_y is the value of initial x and is updated after each call
* @param x is current iteration at which the function needs to be evaluated
* @param h is the step value
* @returns the value of y at thr required value of x from the initial conditions
*/
double rungeKutta(double init_x, const double &init_y, const double &x, const double &h)
{
// Count number of iterations
// using step size or
// step height h
// n calucates the number of iterations
// k1, k2, k3, k4 are the Runge Kutta variables
// used for calculation of y at each iteration
auto n = static_cast<uint64_t>((x - init_x) / h);
// used a vector container for the variables
std::vector<double> k(4,0.0);
// Iterate for number of iterations
double y = init_y;
for (int i=1; i<=n; ++i)
{
// Apply Runge Kutta Formulas
// to find next value of y
k[0] = h*change(init_x, y);
k[1] = h*change(init_x + 0.5*h, y + 0.5*k[0]);
k[2] = h*change(init_x + 0.5*h, y + 0.5*k[1]);
k[3] = h*change(init_x + h, y + k[2]);
// Update next value of y
y += (1.0/6.0)*(k[0] + 2*k[1] + 2*k[2] + k[3]);
// Update next value of x
init_x += h;
}
return y;
}
} // namespace runge_kutta
} // namespace numerical_methods
/**
* @brief Tests to check algorithm implementation.
* @returns void
*/
static void test()
{
std::cout << "The Runge Kutta function will be tested on the basis of precomputed values\n";
std::cout << "Test 1...." << "\n";
double valfirst=numerical_methods::runge_kutta::rungeKutta(2,3,4,0.2); // Tests the function with pre calculated values
assert(valfirst==3.10363932323749570);
std::cout << "Passed Test 1\n";
std::cout << "Test 2...." << "\n";
double valsec=numerical_methods::runge_kutta::rungeKutta(1,2,5,0.1); // The value of step changed
assert(valsec==3.40600589380261409);
std::cout << "Passed Test 2\n";
std::cout << "Test 3...." << "\n";
double valthird=numerical_methods::runge_kutta::rungeKutta(-1,3,4,0.1); // Tested with negative value
assert(valthird==2.49251005860244268);
std::cout << "Passed Test 3\n";
}
/**
* @brief Main function
* @returns 0 on exit
*/
int main()
{
test(); // Execute the tests
return 0;
}