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@(@\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }@)@
Gradient of Determinant Using Expansion by Minors: Example and Test
// Complex examples should supppress conversion warnings
# include <cppad/wno_conversion.hpp>

# include <cppad/cppad.hpp>
# include <cppad/speed/det_by_minor.hpp>
# include <complex>


typedef std::complex<double>     Complex;
typedef CppAD::AD<Complex>       ADComplex;
typedef CPPAD_TESTVECTOR(ADComplex)   ADVector;

// ----------------------------------------------------------------------------

bool JacMinorDet(void)
{     bool ok = true;

     using namespace CppAD;

     size_t n = 2;

     // object for computing determinant
     det_by_minor<ADComplex> Det(n);

     // independent and dependent variable vectors
     CPPAD_TESTVECTOR(ADComplex)  X(n * n);
     CPPAD_TESTVECTOR(ADComplex)  D(1);

     // value of the independent variable
     size_t i;
     for(i = 0; i < n * n; i++)
          X[i] = Complex(int(i), -int(i));

     // set the independent variables
     Independent(X);

     // comupute the determinant
     D[0] = Det(X);

     // create the function object
     ADFun<Complex> f(X, D);

     // argument value
     CPPAD_TESTVECTOR(Complex)     x( n * n );
     for(i = 0; i < n * n; i++)
          x[i] = Complex(2 * i, i);

     // first derivative of the determinant
     CPPAD_TESTVECTOR(Complex) J( n * n );
     J = f.Jacobian(x);

     /*
     f(x)     = x[0] * x[3] - x[1] * x[2]
     f'(x)    = ( x[3], -x[2], -x[1], x[0] )
     */
     Complex Jtrue[] = { x[3], -x[2], -x[1], x[0] };
     for(i = 0; i < n * n; i++)
          ok &= Jtrue[i] == J[i];

     return ok;

}

Input File: example/general/jac_minor_det.cpp