## C++, , ode_semi_implicit_euler.cpp

/**
* \file
* \authors [Krishna Vedala](https://github.com/kvedala)
* \brief Solve a multivariable first order [ordinary differential equation
* (ODEs)](https://en.wikipedia.org/wiki/Ordinary_differential_equation) using
* [semi implicit Euler
* method](https://en.wikipedia.org/wiki/Semi-implicit_Euler_method)
*
* \details
* The ODE being solved is:
* \f{eqnarray*}{
* \dot{u} &=& v\\
* \dot{v} &=& -\omega^2 u\\
* \omega &=& 1\\
* [x_0, u_0, v_0] &=& [0,1,0]\qquad\ldots\text{(initial values)}
* \f}
* The exact solution for the above problem is:
* \f{eqnarray*}{
* u(x) &=& \cos(x)\\
* v(x) &=& -\sin(x)\\
* \f}
* The computation results are stored to a text file semi_implicit_euler.csv
* and the exact soltuion results in exact.csv for comparison. <img
* src="https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/numerical_methods/ode_semi_implicit_euler.svg"
* alt="Implementation solution"/>
*
* To implement [Van der Pol
* oscillator](https://en.wikipedia.org/wiki/Van_der_Pol_oscillator), change the
* ::problem function to:
* cpp
* const double mu = 2.0;
* dy[0] = y[1];
* dy[1] = mu * (1.f - y[0] * y[0]) * y[1] - y[0];
* 
* \see ode_midpoint_euler.cpp, ode_forward_euler.cpp
*/

#include <cmath>
#include <ctime>
#include <fstream>
#include <iostream>
#include <valarray>

/**
* @brief Problem statement for a system with first-order differential
* equations. Updates the system differential variables.
* \note This function can be updated to and ode of any order.
*
* @param[in] 		x 		independent variable(s)
* @param[in,out]	y		dependent variable(s)
* @param[in,out]	dy	    first-derivative of dependent variable(s)
*/
void problem(const double &x, std::valarray<double> *y,
std::valarray<double> *dy) {
const double omega = 1.F;             // some const for the problem
dy[0][0] = y[0][1];                   // x dot
dy[0][1] = -omega * omega * y[0][0];  // y dot
}

/**
* @brief Exact solution of the problem. Used for solution comparison.
*
* @param[in] 		x 		independent variable
* @param[in,out]	y		dependent variable
*/
void exact_solution(const double &x, std::valarray<double> *y) {
y[0][0] = std::cos(x);
y[0][1] = -std::sin(x);
}

/** \addtogroup ode Ordinary Differential Equations
* @{
*/
/**
* @brief Compute next step approximation using the semi-implicit-Euler
* method. @f[y_{n+1}=y_n + dx\cdot f\left(x_n,y_n\right)@f]
* @param[in] 		dx	step size
* @param[in] 	    x	take \f$x_n\f$ and compute \f$x_{n+1}\f$
* @param[in,out] 	y	take \f$y_n\f$ and compute \f$y_{n+1}\f$
* @param[in,out]	dy	compute \f$f\left(x_n,y_n\right)\f$
*/
void semi_implicit_euler_step(const double dx, const double &x,
std::valarray<double> *y,
std::valarray<double> *dy) {
problem(x, y, dy);         // update dy once
y[0][0] += dx * dy[0][0];  // update y0
problem(x, y, dy);         // update dy once more

dy[0][0] = 0.f;      // ignore y0
y[0] += dy[0] * dx;  // update remaining using new dy
}

/**
* @brief Compute approximation using the semi-implicit-Euler
* method in the given limits.
* @param[in] 		dx  	step size
* @param[in]   	x0  	initial value of independent variable
* @param[in] 	    x_max	final value of independent variable
* @param[in,out] 	y	    take \f$y_n\f$ and compute \f$y_{n+1}\f$
* @param[in] save_to_file	flag to save results to a CSV file (1) or not (0)
* @returns time taken for computation in seconds
*/
double semi_implicit_euler(double dx, double x0, double x_max,
std::valarray<double> *y,
bool save_to_file = false) {
std::valarray<double> dy = y[0];

std::ofstream fp;
if (save_to_file) {
fp.open("semi_implicit_euler.csv", std::ofstream::out);
if (!fp.is_open()) {
std::perror("Error! ");
}
}

std::size_t L = y->size();

/* start integration */
std::clock_t t1 = std::clock();
double x = x0;
do {  // iterate for each step of independent variable
if (save_to_file && fp.is_open()) {
// write to file
fp << x << ",";
for (int i = 0; i < L - 1; i++) {
fp << y[0][i] << ",";
}
fp << y[0][L - 1] << "\n";
}

semi_implicit_euler_step(dx, x, y, &dy);  // perform integration
x += dx;                                  // update step
} while (x <= x_max);  // till upper limit of independent variable
/* end of integration */
std::clock_t t2 = std::clock();

if (fp.is_open())
fp.close();

return static_cast<double>(t2 - t1) / CLOCKS_PER_SEC;
}

/** @} */

/**
* Function to compute and save exact solution for comparison
*
* \param [in]    X0  	    initial value of independent variable
* \param [in] 	  X_MAX	    final value of independent variable
* \param [in] 	  step_size	independent variable step size
* \param [in]    Y0	    initial values of dependent variables
*/
void save_exact_solution(const double &X0, const double &X_MAX,
const double &step_size,
const std::valarray<double> &Y0) {
double x = X0;
std::valarray<double> y = Y0;

std::ofstream fp("exact.csv", std::ostream::out);
if (!fp.is_open()) {
std::perror("Error! ");
return;
}
std::cout << "Finding exact solution\n";

std::clock_t t1 = std::clock();
do {
fp << x << ",";
for (int i = 0; i < y.size() - 1; i++) {
fp << y[i] << ",";
}
fp << y[y.size() - 1] << "\n";

exact_solution(x, &y);

x += step_size;
} while (x <= X_MAX);

std::clock_t t2 = std::clock();
double total_time = static_cast<double>(t2 - t1) / CLOCKS_PER_SEC;
std::cout << "\tTime = " << total_time << " ms\n";

fp.close();
}

/**
* Main Function
*/
int main(int argc, char *argv[]) {
double X0 = 0.f;                       /* initial value of x0 */
double X_MAX = 10.F;                   /* upper limit of integration */
std::valarray<double> Y0 = {1.f, 0.f}; /* initial value Y = y(x = x_0) */
double step_size;

if (argc == 1) {
std::cout << "\nEnter the step size: ";
std::cin >> step_size;
} else {
// use commandline argument as independent variable step size
step_size = std::atof(argv[1]);
}

// get approximate solution
double total_time = semi_implicit_euler(step_size, X0, X_MAX, &Y0, true);
std::cout << "\tTime = " << total_time << " ms\n";

/* compute exact solution for comparion */
save_exact_solution(X0, X_MAX, step_size, Y0);

return 0;
}