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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 .
Atomic Eigen Cholesky Factorization: Example and Test

Description
The ADFun function object f for this example is @[@ f(x) = \R{chol} \left( \begin{array}{cc} x_0 & x_1 \\ x_1 & x_2 \end{array} \right) = \frac{1}{ \sqrt{x_0} } \left( \begin{array}{cc} x_0 & 0 \\ x_1 & \sqrt{ x_0 x_2 - x_1 x_1 } \end{array} \right) @]@ where the matrix is positive definite; i.e., @(@ x_0 > 0 @)@, @(@ x_2 > 0 @)@ and @(@ x_0 x_2 - x_1 x_1 > 0 @)@.

Contents
cholesky_theoryAD Theory for Cholesky Factorization
atomic_two_eigen_cholesky.hppatomic_two Eigen Cholesky Factorization Class

Use Atomic Function
# include <cppad/cppad.hpp>
# include <cppad/example/atomic_two/eigen_cholesky.hpp>


bool eigen_cholesky(void)
{
    typedef double scalar;
    typedef atomic_eigen_cholesky<scalar>::ad_scalar ad_scalar;
    typedef atomic_eigen_cholesky<scalar>::ad_matrix ad_matrix;
    //
    bool ok    = true;
    scalar eps = 10. * std::numeric_limits<scalar>::epsilon();
    using CppAD::NearEqual;
    //

Constructor
    // -------------------------------------------------------------------
    // object that computes cholesky factor of a matrix
    atomic_eigen_cholesky<scalar> cholesky;
    // -------------------------------------------------------------------
    // declare independent variable vector x
    size_t n = 3;
    CPPAD_TESTVECTOR(ad_scalar) ad_x(n);
    ad_x[0] = 2.0;
    ad_x[1] = 0.5;
    ad_x[2] = 3.0;
    CppAD::Independent(ad_x);
    // -------------------------------------------------------------------
    // A = [ x[0]  x[1] ]
    //     [ x[1]  x[2] ]
    size_t nr  = 2;
    ad_matrix ad_A(nr, nr);
    ad_A(0, 0) = ad_x[0];
    ad_A(1, 0) = ad_x[1];
    ad_A(0, 1) = ad_x[1];
    ad_A(1, 1) = ad_x[2];
    // -------------------------------------------------------------------
    // use atomic operation to L such that A = L * L^T
    ad_matrix ad_L = cholesky.op(ad_A);
    // -------------------------------------------------------------------
    // declare the dependent variable vector y
    size_t m = 3;
    CPPAD_TESTVECTOR(ad_scalar) ad_y(m);
    ad_y[0] = ad_L(0, 0);
    ad_y[1] = ad_L(1, 0);
    ad_y[2] = ad_L(1, 1);
    CppAD::ADFun<scalar> f(ad_x, ad_y);
    // -------------------------------------------------------------------
    // check zero order forward mode
    CPPAD_TESTVECTOR(scalar) x(n), y(m);
    x[0] = 2.0;
    x[1] = 0.5;
    x[2] = 5.0;
    y   = f.Forward(0, x);
    scalar check;
    check = std::sqrt( x[0] );
    ok   &= NearEqual(y[0], check, eps, eps);
    check = x[1] / std::sqrt( x[0] );
    ok   &= NearEqual(y[1], check, eps, eps);
    check = std::sqrt( x[2] - x[1] * x[1] / x[0] );
    ok   &= NearEqual(y[2], check, eps, eps);
    // -------------------------------------------------------------------
    // check first order forward mode
    CPPAD_TESTVECTOR(scalar) x1(n), y1(m);
    //
    // partial w.r.t. x[0]
    x1[0] = 1.0;
    x1[1] = 0.0;
    x1[2] = 0.0;
    //
    y1    = f.Forward(1, x1);
    check = 1.0 / (2.0 * std::sqrt( x[0] ) );
    ok   &= NearEqual(y1[0], check, eps, eps);
    //
    check = - x[1] / (2.0 * x[0] * std::sqrt( x[0] ) );
    ok   &= NearEqual(y1[1], check, eps, eps);
    //
    check = std::sqrt( x[2] - x[1] * x[1] / x[0] );
    check = x[1] * x[1] / (x[0] * x[0] * 2.0 * check);
    ok   &= NearEqual(y1[2], check, eps, eps);
    //
    // partial w.r.t. x[1]
    x1[0] = 0.0;
    x1[1] = 1.0;
    x1[2] = 0.0;
    //
    y1    = f.Forward(1, x1);
    ok   &= NearEqual(y1[0], 0.0, eps, eps);
    //
    check = 1.0 / std::sqrt( x[0] );
    ok   &= NearEqual(y1[1], check, eps, eps);
    //
    check = std::sqrt( x[2] - x[1] * x[1] / x[0] );
    check = - 2.0 * x[1] / (2.0 * check * x[0] );
    ok   &= NearEqual(y1[2], check, eps, eps);
    //
    // partial w.r.t. x[2]
    x1[0] = 0.0;
    x1[1] = 0.0;
    x1[2] = 1.0;
    //
    y1    = f.Forward(1, x1);
    ok   &= NearEqual(y1[0], 0.0, eps, eps);
    ok   &= NearEqual(y1[1], 0.0, eps, eps);
    //
    check = std::sqrt( x[2] - x[1] * x[1] / x[0] );
    check = 1.0 / (2.0 * check);
    ok   &= NearEqual(y1[2], check, eps, eps);
    // -------------------------------------------------------------------
    // check second order forward mode
    CPPAD_TESTVECTOR(scalar) x2(n), y2(m);
    //
    // second partial w.r.t x[2]
    x2[0] = 0.0;
    x2[1] = 0.0;
    x2[2] = 0.0;
    y2    = f.Forward(2, x2);
    ok   &= NearEqual(y2[0], 0.0, eps, eps);
    ok   &= NearEqual(y2[1], 0.0, eps, eps);
    //
    check = std::sqrt( x[2] - x[1] * x[1] / x[0] );  // funciton value
    check = - 1.0 / ( 4.0 * check * check * check ); // second derivative
    check = 0.5 * check;                             // taylor coefficient
    ok   &= NearEqual(y2[2], check, eps, eps);
    // -------------------------------------------------------------------
    // check first order reverse mode
    CPPAD_TESTVECTOR(scalar) w(m), d1w(n);
    w[0] = 0.0;
    w[1] = 0.0;
    w[2] = 1.0;
    d1w  = f.Reverse(1, w);
    //
    // partial of f[2] w.r.t x[0]
    scalar f2    = std::sqrt( x[2] - x[1] * x[1] / x[0] );
    scalar f2_x0 = x[1] * x[1] / (2.0 * f2 * x[0] * x[0] );
    ok          &= NearEqual(d1w[0], f2_x0, eps, eps);
    //
    // partial of f[2] w.r.t x[1]
    scalar f2_x1 = - x[1] / (f2 * x[0] );
    ok          &= NearEqual(d1w[1], f2_x1, eps, eps);
    //
    // partial of f[2] w.r.t x[2]
    scalar f2_x2 = 1.0 / (2.0 * f2 );
    ok          &= NearEqual(d1w[2], f2_x2, eps, eps);
    // -------------------------------------------------------------------
    // check second order reverse mode
    CPPAD_TESTVECTOR(scalar) d2w(2 * n);
    d2w  = f.Reverse(2, w);
    //
    // check first order results
    ok &= NearEqual(d2w[0 * 2 + 0], f2_x0, eps, eps);
    ok &= NearEqual(d2w[1 * 2 + 0], f2_x1, eps, eps);
    ok &= NearEqual(d2w[2 * 2 + 0], f2_x2, eps, eps);
    //
    // check second order results
    scalar f2_x2_x0 = - 0.5 * f2_x0 / (f2 * f2 );
    ok             &= NearEqual(d2w[0 * 2 + 1], f2_x2_x0, eps, eps);
    scalar f2_x2_x1 = - 0.5 * f2_x1 / (f2 * f2 );
    ok             &= NearEqual(d2w[1 * 2 + 1], f2_x2_x1, eps, eps);
    scalar f2_x2_x2 = - 0.5 * f2_x2 / (f2 * f2 );
    ok             &= NearEqual(d2w[2 * 2 + 1], f2_x2_x2, eps, eps);
    // -------------------------------------------------------------------
    // check third order reverse mode
    CPPAD_TESTVECTOR(scalar) d3w(3 * n);
    d3w  = f.Reverse(3, w);
    //
    // check first order results
    ok &= NearEqual(d3w[0 * 3 + 0], f2_x0, eps, eps);
    ok &= NearEqual(d3w[1 * 3 + 0], f2_x1, eps, eps);
    ok &= NearEqual(d3w[2 * 3 + 0], f2_x2, eps, eps);
    //
    // check second order results
    ok             &= NearEqual(d3w[0 * 3 + 1], f2_x2_x0, eps, eps);
    ok             &= NearEqual(d3w[1 * 3 + 1], f2_x2_x1, eps, eps);
    ok             &= NearEqual(d3w[2 * 3 + 1], f2_x2_x2, eps, eps);
    // -------------------------------------------------------------------
    scalar f2_x2_x2_x0 = - 0.5 * f2_x2_x0 / (f2 * f2);
    f2_x2_x2_x0 += f2_x2 * f2_x0 / (f2 * f2 * f2);
    ok          &= NearEqual(d3w[0 * 3 + 2], 0.5 * f2_x2_x2_x0, eps, eps);
    scalar f2_x2_x2_x1 = - 0.5 * f2_x2_x1 / (f2 * f2);
    f2_x2_x2_x1 += f2_x2 * f2_x1 / (f2 * f2 * f2);
    ok          &= NearEqual(d3w[1 * 3 + 2], 0.5 * f2_x2_x2_x1, eps, eps);
    scalar f2_x2_x2_x2 = - 0.5 * f2_x2_x2 / (f2 * f2);
    f2_x2_x2_x2 += f2_x2 * f2_x2 / (f2 * f2 * f2);
    ok          &= NearEqual(d3w[2 * 3 + 2], 0.5 * f2_x2_x2_x2, eps, eps);
    return ok;
}

Input File: example/atomic_two/eigen_cholesky.cpp