jac_lu_det.cpp

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Gradient of Determinant Using Lu Factorization: Example and Test

// Complex examples should supppress conversion warnings
# include <cppad/wno_conversion.hpp>

# include <cppad/cppad.hpp>
# include <cppad/speed/det_by_lu.hpp>

// The AD complex case is used by this example so must
// define a specializatgion of LeqZero,AbsGeq for the AD<Complex> case
namespace CppAD {
   CPPAD_BOOL_BINARY( std::complex<double> ,  AbsGeq   )
   CPPAD_BOOL_UNARY(  std::complex<double> ,  LeqZero )
}

bool JacLuDet(void)
{  bool ok = true;
   using namespace CppAD;
   double eps99 = 99.0 * std::numeric_limits<double>::epsilon();

   typedef std::complex<double> Complex;
   typedef AD<Complex>          ADComplex;

   size_t n = 2;

   // object for computing determinants
   det_by_lu<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);

   // compute 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]
   */
   Complex Jtrue[]  = { x[3], -x[2], -x[1], x[0] };
   for( i = 0; i < n*n; i++)
      ok &= NearEqual( Jtrue[i], J[i], eps99 , eps99 );

   return ok;
}