#include <stdafx.h>
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <optimization.h>

using namespace alglib;

double function_z(void *data_for_calculation)
{
    return 1.2345678;
}

void function1_grad(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr)
{
    // ptr contains pointer to data required to calculate Z, you can pass it to external function
    func = function_z(ptr)*pow(x[0]+3,4) + pow(x[1]-3,4);
    grad[0] = 4*function_z(ptr)*pow(x[0]+3,3);
    grad[1] = 4*pow(x[1]-3,3);
}

int main(int argc, char **argv)
{
    //
    // This example demonstrates minimization of f(x,y) = 100*(x+3)^4+(y-3)^4
    // with nonlinear conjugate gradient method.
    //
    real_1d_array x = "[0,0]";
    double epsg = 0.0000000001;
    double epsf = 0;
    double epsx = 0;
    ae_int_t maxits = 0;
    mincgstate state;
    mincgreport rep;

    void *data_for_calculation = .... ; // pointer to data required to calculate Z

    mincgcreate(x, state);
    mincgsetcond(state, epsg, epsf, epsx, maxits);
    mincgoptimize(state, function1_grad, NULL, data_for_calculation); // you pass pointer to additional data required to calculate Z
    mincgresults(state, x, rep);

    printf("%d\n", int(rep.terminationtype)); // EXPECTED: 4
    printf("%s\n", x.tostring(2).c_str()); // EXPECTED: [-3,3]
    return 0;
}

void function1_grad(const real_1d_array &L, double &func, real_1d_array &grad, void *ptr)

void function1_grad(const real_1d_array &L, double &func, real_1d_array &grad, void *ptr, ... + params)