\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)
min_nso_linear.cpp¶
View page sourceabs_normal min_nso_linear: Example and Test¶
Purpose¶
We minimize the function \(f : \B{R}^3 \rightarrow \B{R}\) defined by
\begin{eqnarray}
f( x_0, x_1, x_2 ) & = & x_0^2 + 2 (x_0 + x_1)^2 + | x_2 |
\end{eqnarray}
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;
}