/* nag::ad::c05ay Adjoint Example Program.
*/
#include <dco.hpp>
#include <iostream>
#include <nagad.h>
// Function which calls NAG AD Library routines.
template <typename T> void func(T r, T &x);
// Driver with the adjoint calls.
// Finds the zero point f(xv) = 0 for the function
// f(x) = exp(-x) - r*x.
// Also, computes the gradient dxdr = dxv/dr using a symbolic adjoint of the
// root finder
void driver(const double &rv, double &xv, double &dxdr);
int main()
{
std::cout << " nag::ad::c05ay Adjoint Example Program Results\n";
// Set problem parameters
double rv = 2.0;
// Solution x
double xv;
// Derivative of x
double dxdr;
// Call driver
driver(rv, xv, dxdr);
// sPrint outputs
std::cout << "\n Derivatives calculated: First order adjoints\n";
std::cout << " Computational mode : symbolic (non-expert mode)\n";
// Print derivatives
std::cout.setf(std::ios::scientific, std::ios::floatfield);
std::cout.precision(12);
std::cout << "\n Solution:\n";
std::cout << " x = " << xv << std::endl;
std::cout << "\n Derivative of solution x w.r.t. parameter r:\n";
std::cout << " dx/dr(x) = " << dxdr << std::endl;
return 0;
}
// Driver with the adjoint calls.
// Finds the zero point f(xv) = 0 for the function
// f(x) = exp(-x) - r*x.
// Also, computes the gradient dxdr = dxv/dr using a symbolic adjoint of the
// root finder
void driver(const double &rv, double &xv, double &dxdr)
{
using T = dco::ga1s<double>::type;
// Create the AD tape
dco::ga1s<double>::global_tape = dco::ga1s<double>::tape_t::create();
// Function parameter r
T r = rv;
// Register variables to differentiate w.r.t.
dco::ga1s<double>::global_tape->register_variable(r);
// Variable to differentiate
T x;
// Call the NAG AD Lib functions
func(r, x);
// Extract the computed solution
xv = dco::value(x);
dco::ga1s<double>::global_tape->register_output_variable(x);
dco::derivative(x) = 1.0;
dco::ga1s<double>::global_tape->interpret_adjoint();
// Extract the derivatives
dxdr = dco::derivative(r);
// Remove tape
dco::ga1s<double>::tape_t::remove(dco::ga1s<double>::global_tape);
}
// Function which calls NAG AD Library routines
template <typename T> void func(T r, T &x)
{
// Active variables
T a = 0.0, b = 1.0;
T eps = 1.0e-5, eta = 0.0;
// Create AD configuration data object
Integer ifail = 0;
nag::ad::handle_t ad_handle;
// Set computational mode:
ad_handle.set_strategy(nag::ad::symbolic);
// Routine for computing an approximation to a simple zero of the function f
ifail = 0;
nag::ad::c05ay(ad_handle, a, b, eps, eta,
[&](nag::ad::handle_t const& handle, T const& x, T & z) {
z = exp(-x) - x * r;
},
x, ifail);
}