/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-20 Bradley M. Bell

CppAD is distributed under the terms of the
             Eclipse Public License Version 2.0.

This Source Code may also be made available under the following
Secondary License when the conditions for such availability set forth
in the Eclipse Public License, Version 2.0 are satisfied:
      GNU General Public License, Version 2.0 or later.
---------------------------------------------------------------------------- */

/*
$begin min_nso_linear.cpp$$
$spell
    nso
    smo
$$

$section abs_normal min_nso_linear: Example and Test$$

$head Purpose$$
We minimize the function
$latex f : \B{R}^3 \rightarrow \B{R}$$ defined by
$latex \[
\begin{array}{rcl}
f( x_0, x_1, x_2  ) & = & x_0^2 + 2 (x_0 + x_1)^2 + | x_2 |
\end{array}
\] $$

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


$head Source$$
$srcthisfile%
    0%// BEGIN C++%// END C++%
1%$$

$end
-------------------------------------------------------------------------------
*/
// BEGIN C++
# 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;
}
// END C++
