\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)

det_by_lu.cpp

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Determinant Using Lu Factorization: Example and Test

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

bool det_by_lu()
{   bool ok = true;
    double eps99 = 99.0 * std::numeric_limits<double>::epsilon();

    // dimension of the matrix
    size_t n = 3;

    // construct the determinat object
    CppAD::det_by_lu<double> Det(n);

    double  a[] = {
        1., 2., 3.,  // a[0] a[1] a[2]
        3., 2., 1.,  // a[3] a[4] a[5]
        2., 1., 2.   // a[6] a[7] a[8]
    };
    CPPAD_TESTVECTOR(double) A(9);
    size_t i;
    for(i = 0; i < 9; i++)
        A[i] = a[i];


    // evaluate the determinant
    double det = Det(A);

    double check;
    check = a[0]*(a[4]*a[8] - a[5]*a[7])
             - a[1]*(a[3]*a[8] - a[5]*a[6])
             + a[2]*(a[3]*a[7] - a[4]*a[6]);

    ok = CppAD::NearEqual(det, check, eps99, eps99);

    return ok;
}