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<?xml version='1.0'?>
<html xmlns='http://www.w3.org/1999/xhtml'
      xmlns:math='http://www.w3.org/1998/Math/MathML'
>
<head>
<title>sparse_hes_fun: Example and test</title>
<meta http-equiv='Content-Type' content='text/html' charset='utf-8'/>
<meta name="description" id="description" content="sparse_hes_fun: Example and test"/>
<meta name="keywords" id="keywords" content=" sparse_hes_fun example test sparse_hes_fun: and "/>
<style type='text/css'>
body { color : black }
body { background-color : white }
A:link { color : blue }
A:visited { color : purple }
A:active { color : purple }
</style>
<script type='text/javascript' language='JavaScript' src='_sparse_hes_fun.cpp_xml.js'>
</script>
</head>
<body>
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<center><b><big><big>sparse_hes_fun: Example and test</big></big></b></center>
<code><font color="blue"><pre style='display:inline'> 
# include &lt;cppad/speed/sparse_hes_fun.hpp&gt;
# include &lt;cppad/speed/uniform_01.hpp&gt;
# include &lt;cppad/cppad.hpp&gt;

bool sparse_hes_fun(void)
{	using CppAD::NearEqual;
	bool ok = true;

	typedef CppAD::<a href="ad.xml" target="_top">AD</a>&lt;double&gt; ADScalar;

	size_t i, j, k;
	double eps = 10. * CppAD::numeric_limits&lt;double&gt;::epsilon();
	size_t n   = 3;
	size_t m   = 1;
	size_t K   = 5;
	CppAD::vector&lt;size_t&gt;       row(K),  col(K);
	CppAD::vector&lt;double&gt;       x(n),    yp(n * n);
	CppAD::vector&lt;ADScalar&gt;     a_x(n),  a_y(m);

	// choose x
	for(j = 0; j &lt; n; j++)
		a_x[j] = x[j] = double(j + 1);

	// choose row, col
	for(k = 0; k &lt; K; k++)
	{	row[k] = k % n;
		col[k] = (K - k) % n;
	}

	// declare independent variables
	<a href="independent.xml" target="_top">Independent</a>(a_x);

	// evaluate function
	size_t order = 0;
	CppAD::sparse_hes_fun&lt;ADScalar&gt;(n, a_x, row, col, order, a_y);

	// evaluate Hessian
	order = 2;
	CppAD::sparse_hes_fun&lt;double&gt;(n, x, row, col, order, yp);

	// use AD to evaluate Hessian
	CppAD::<a href="funconstruct.xml" target="_top">ADFun</a>&lt;double&gt;   f(a_x, a_y);
	CppAD::vector&lt;double&gt;  hes(n * n);
	// compoute Hessian of f_0 (x)
	hes = f.Hessian(x, 0);

	for(i = 0; i &lt; n; i++)
	{	for(j = 0; j &lt; n; j++)
			ok &amp;= <a href="nearequal.xml" target="_top">NearEqual</a>(hes[i * n + j], yp[i * n + j] , eps, eps);
	}
	return ok;
}
</pre>

</font></code>


<hr/>Input File: speed/example/sparse_hes_fun.cpp

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