abs_normal min_nso_linear: Example and Test

min_nso_linear.cpp

@(@\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 . abs_normal min_nso_linear: Example and Test
Purpose
We minimize the function @(@ f : \B{R}^3 \rightarrow \B{R} @)@ defined by @[@ \begin{array}{rcl} f( x_0, x_1, x_2 ) & = & x_0^2 + 2 (x_0 + x_1)^2 + | x_2 | \end{array} @]@

Discussion
This routine uses abs_min_linear which uses lp_box , a linear programming algorithm. It is mean to be compared with min_nso_quad.cpp which uses a quadratic programing algorithm for the same problem. To see this comparison, set level = 1 is both examples.

Source


# include <cppad/cppad.hpp>
# include "min_nso_linear.hpp"

bool min_nso_linear(void)
{   bool ok = true;
    //
    using CppAD::AD;
    using CppAD::ADFun;
    //
    typedef CPPAD_TESTVECTOR(size_t)       s_vector;
    typedef CPPAD_TESTVECTOR(double)       d_vector;
    typedef CPPAD_TESTVECTOR( AD<double> ) ad_vector;
    //
    size_t level = 0;    // level of tracing
    size_t n     = 3;    // size of x
    size_t m     = 1;    // size of y
    size_t s     = 1;    // number of data points and absolute values
    //
    // start recording the function f(x)
    ad_vector ax(n), ay(m);
    for(size_t j = 0; j < n; j++)
        ax[j] = double(j + 1);
    Independent( ax );
    //
    ay[0]  =  ax[0] * ax[0];
    ay[0] += 2.0 * (ax[0] + ax[1]) * (ax[0] + ax[1]);
    ay[0] += fabs( ax[2] );
    ADFun<double> f(ax, ay);
    //
    // create its abs_normal representation in g, a
    ADFun<double> g, a;
    f.abs_normal_fun(g, a);

    // check dimension of domain and range space for g
    ok &= g.Domain() == n + s;
    ok &= g.Range()  == m + s;

    // check dimension of domain and range space for a
    ok &= a.Domain() == n;
    ok &= a.Range()  == s;

    // epsilon
    d_vector epsilon(2);
    double eps = 1e-3;
    epsilon[0] = eps;
    epsilon[1] = eps;

    // maxitr
    s_vector maxitr(3);
    maxitr[0] = 100;
    maxitr[1] = 20;
    maxitr[2] = 20;

    // b_in
    double b_in = 1.0;

    // call min_nso_linear
    d_vector x_in(n), x_out(n);
    for(size_t j = 0; j < n; j++)
        x_in[j]  = double(j + 1);

    //
    ok &= CppAD::min_nso_linear(
        level, g, a, epsilon, maxitr, b_in, x_in, x_out
    );
    //
    for(size_t j = 0; j < n; j++)
        ok &= std::fabs( x_out[j] ) < eps;

    return ok;
}

Input File: example/abs_normal/min_nso_linear.cpp