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// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-22 Bradley M. Bell
// ----------------------------------------------------------------------------
/*
{xrst_begin atomic_two_eigen_mat_mul.cpp app}
Atomic Eigen Matrix Multiply: Example and Test
##############################################
Description
***********
The :ref:`ADFun-name` function object *f* for this example is
.. math::
f(x) =
\left( \begin{array}{cc}
0 & 0 \\
1 & 2 \\
x_0 & x_1
\end{array} \right)
\left( \begin{array}{c}
x_0 \\
x_1
\end{array} \right)
=
\left( \begin{array}{c}
0 \\
x_0 + 2 x_1 \\
x_0 x_0 + x_1 x_1 )
\end{array} \right)
{xrst_toc_hidden
include/cppad/example/atomic_two/eigen_mat_mul.hpp
}
Class Definition
****************
This example uses the file :ref:`atomic_two_eigen_mat_mul.hpp-name`
which defines matrix multiply as a :ref:`atomic_two-name` operation.
Use Atomic Function
*******************
{xrst_spell_off}
{xrst_code cpp} */
# include <cppad/cppad.hpp>
# include <cppad/example/atomic_two/eigen_mat_mul.hpp>
bool eigen_mat_mul(void)
{ //
typedef double scalar;
typedef CppAD::AD<scalar> ad_scalar;
typedef atomic_eigen_mat_mul<scalar>::ad_matrix ad_matrix;
//
bool ok = true;
scalar eps = 10. * std::numeric_limits<scalar>::epsilon();
using CppAD::NearEqual;
//
/* {xrst_code}
{xrst_spell_on}
Constructor
===========
{xrst_spell_off}
{xrst_code cpp} */
// -------------------------------------------------------------------
// object that multiplies arbitrary matrices
atomic_eigen_mat_mul<scalar> mat_mul;
// -------------------------------------------------------------------
// declare independent variable vector x
size_t n = 2;
CPPAD_TESTVECTOR(ad_scalar) ad_x(n);
for(size_t j = 0; j < n; j++)
ad_x[j] = ad_scalar(j);
CppAD::Independent(ad_x);
// -------------------------------------------------------------------
// [ 0 0 ]
// left = [ 1 2 ]
// [ x[0] x[1] ]
size_t nr_left = 3;
size_t n_middle = 2;
ad_matrix ad_left(nr_left, n_middle);
ad_left(0, 0) = ad_scalar(0.0);
ad_left(0, 1) = ad_scalar(0.0);
ad_left(1, 0) = ad_scalar(1.0);
ad_left(1, 1) = ad_scalar(2.0);
ad_left(2, 0) = ad_x[0];
ad_left(2, 1) = ad_x[1];
// -------------------------------------------------------------------
// right = [ x[0] , x[1] ]^T
size_t nc_right = 1;
ad_matrix ad_right(n_middle, nc_right);
ad_right(0, 0) = ad_x[0];
ad_right(1, 0) = ad_x[1];
// -------------------------------------------------------------------
// use atomic operation to multiply left * right
ad_matrix ad_result = mat_mul.op(ad_left, ad_right);
// -------------------------------------------------------------------
// check that first component of result is a parameter
// and the other components are variables.
ok &= Parameter( ad_result(0, 0) );
ok &= Variable( ad_result(1, 0) );
ok &= Variable( ad_result(2, 0) );
// -------------------------------------------------------------------
// declare the dependent variable vector y
size_t m = 3;
CPPAD_TESTVECTOR(ad_scalar) ad_y(m);
for(size_t i = 0; i < m; i++)
ad_y[i] = ad_result(long(i), 0);
CppAD::ADFun<scalar> f(ad_x, ad_y);
// -------------------------------------------------------------------
// check zero order forward mode
CPPAD_TESTVECTOR(scalar) x(n), y(m);
for(size_t i = 0; i < n; i++)
x[i] = scalar(i + 2);
y = f.Forward(0, x);
ok &= NearEqual(y[0], 0.0, eps, eps);
ok &= NearEqual(y[1], x[0] + 2.0 * x[1], eps, eps);
ok &= NearEqual(y[2], x[0] * x[0] + x[1] * x[1], eps, eps);
// -------------------------------------------------------------------
// check first order forward mode
CPPAD_TESTVECTOR(scalar) x1(n), y1(m);
x1[0] = 1.0;
x1[1] = 0.0;
y1 = f.Forward(1, x1);
ok &= NearEqual(y1[0], 0.0, eps, eps);
ok &= NearEqual(y1[1], 1.0, eps, eps);
ok &= NearEqual(y1[2], 2.0 * x[0], eps, eps);
x1[0] = 0.0;
x1[1] = 1.0;
y1 = f.Forward(1, x1);
ok &= NearEqual(y1[0], 0.0, eps, eps);
ok &= NearEqual(y1[1], 2.0, eps, eps);
ok &= NearEqual(y1[2], 2.0 * x[1], eps, eps);
// -------------------------------------------------------------------
// check second order forward mode
CPPAD_TESTVECTOR(scalar) x2(n), y2(m);
x2[0] = 0.0;
x2[1] = 0.0;
y2 = f.Forward(2, x2);
ok &= NearEqual(y2[0], 0.0, eps, eps);
ok &= NearEqual(y2[1], 0.0, eps, eps);
ok &= NearEqual(y2[2], 1.0, eps, eps); // 1/2 * f_1''(x)
// -------------------------------------------------------------------
// check first order reverse mode
CPPAD_TESTVECTOR(scalar) w(m), d1w(n);
w[0] = 0.0;
w[1] = 1.0;
w[2] = 0.0;
d1w = f.Reverse(1, w);
ok &= NearEqual(d1w[0], 1.0, eps, eps);
ok &= NearEqual(d1w[1], 2.0, eps, eps);
w[0] = 0.0;
w[1] = 0.0;
w[2] = 1.0;
d1w = f.Reverse(1, w);
ok &= NearEqual(d1w[0], 2.0 * x[0], eps, eps);
ok &= NearEqual(d1w[1], 2.0 * x[1], eps, eps);
// -------------------------------------------------------------------
// check second order reverse mode
CPPAD_TESTVECTOR(scalar) d2w(2 * n);
d2w = f.Reverse(2, w);
// partial f_2 w.r.t. x_0
ok &= NearEqual(d2w[0 * 2 + 0], 2.0 * x[0], eps, eps);
// partial f_2 w.r.t x_1
ok &= NearEqual(d2w[1 * 2 + 0], 2.0 * x[1], eps, eps);
// partial f_2 w.r.t x_1, x_0
ok &= NearEqual(d2w[0 * 2 + 1], 0.0, eps, eps);
// partial f_2 w.r.t x_1, x_1
ok &= NearEqual(d2w[1 * 2 + 1], 2.0, eps, eps);
// -------------------------------------------------------------------
// check forward Jacobian sparsity
CPPAD_TESTVECTOR( std::set<size_t> ) r(n), s(m);
std::set<size_t> check_set;
for(size_t j = 0; j < n; j++)
r[j].insert(j);
s = f.ForSparseJac(n, r);
check_set.clear();
ok &= s[0] == check_set;
check_set.insert(0);
check_set.insert(1);
ok &= s[1] == check_set;
ok &= s[2] == check_set;
// -------------------------------------------------------------------
// check reverse Jacobian sparsity
r.resize(m);
for(size_t i = 0; i < m; i++)
r[i].insert(i);
s = f.RevSparseJac(m, r);
check_set.clear();
ok &= s[0] == check_set;
check_set.insert(0);
check_set.insert(1);
ok &= s[1] == check_set;
ok &= s[2] == check_set;
// -------------------------------------------------------------------
// check forward Hessian sparsity for f_2 (x)
CPPAD_TESTVECTOR( std::set<size_t> ) r2(1), s2(1), h(n);
for(size_t j = 0; j < n; j++)
r2[0].insert(j);
s2[0].clear();
s2[0].insert(2);
h = f.ForSparseHes(r2, s2);
check_set.clear();
check_set.insert(0);
ok &= h[0] == check_set;
check_set.clear();
check_set.insert(1);
ok &= h[1] == check_set;
// -------------------------------------------------------------------
// check reverse Hessian sparsity for f_2 (x)
CPPAD_TESTVECTOR( std::set<size_t> ) s3(1);
s3[0].clear();
s3[0].insert(2);
h = f.RevSparseHes(n, s3);
check_set.clear();
check_set.insert(0);
ok &= h[0] == check_set;
check_set.clear();
check_set.insert(1);
ok &= h[1] == check_set;
// -------------------------------------------------------------------
return ok;
}
/* {xrst_code}
{xrst_spell_on}
{xrst_end atomic_two_eigen_mat_mul.cpp}
*/
|
