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jac_minor_det.cpp |
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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}} }@)@This is cppad-20221105 documentation. Here is a link to its
current documentation
.
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( double(i), -double(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( double(2 * i) , double(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