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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>Sacado Speed: Gradient of Determinant Using Lu Factorization</title>
<meta http-equiv='Content-Type' content='text/html' charset='utf-8'/>
<meta name="description" id="description" content="Sacado Speed: Gradient of Determinant Using Lu Factorization"/>
<meta name="keywords" id="keywords" content=" sacado speed: gradient of determinant using lu factorization link_det_lu speed matrix factor specifications implementation "/>
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body { color : black }
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<script type='text/javascript' language='JavaScript' src='_sacado_det_lu.cpp_xml.js'>
</script>
</head>
<body>
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<center><b><big><big>Sacado Speed: Gradient of Determinant Using Lu Factorization</big></big></b></center>
<br/>
<b><big><a name="Specifications" id="Specifications">Specifications</a></big></b>
<br/>
See <a href="link_det_lu.xml" target="_top"><span style='white-space: nowrap'>link_det_lu</span></a>
.

<br/>
<br/>
<b><big><a name="Implementation" id="Implementation">Implementation</a></big></b>

<code><font color='blue'><pre style='display:inline'> 
# include &lt;Sacado.hpp&gt;
# include &lt;cppad/speed/det_by_lu.hpp&gt;
# include &lt;cppad/speed/uniform_01.hpp&gt;
# include &lt;cppad/vector.hpp&gt;

// list of possible options
extern bool global_memory, global_onetape, global_atomic, global_optimize;

bool link_det_lu(
	size_t                     size     , 
	size_t                     repeat   , 
	CppAD::vector&lt;double&gt;     &amp;matrix   ,
	CppAD::vector&lt;double&gt;     &amp;gradient )
{
	// speed test global option values
	if( global_onetape || global_atomic )
		return false;
	if( global_memory || global_optimize )
		return false;
	// -----------------------------------------------------
	// setup
	//
	// object for computing determinant
	typedef Sacado::Rad::ADvar&lt;double&gt;   ADScalar; 
	typedef CppAD::vector&lt;ADScalar&gt;      ADVector; 
	CppAD::det_by_lu&lt;ADScalar&gt;           Det(size);

	size_t i;                // temporary index
	size_t n = size * size;  // number of independent variables
	ADScalar   detA;         // AD value of the determinant
	ADVector   A(n);         // AD version of matrix 
	
	// ------------------------------------------------------
	while(repeat--)
	{	// get the next matrix
		CppAD::uniform_01(n, matrix);

		// set independent variable values
		for(i = 0; i &lt; n; i++)
			A[i] = matrix[i];

		// compute the determinant
		detA = Det(A);

		// compute the gradient of detA
		ADScalar::Gradcomp();

		// evaluate and return gradient using reverse mode
		for(i =0; i &lt; n; i++)
			gradient[i] = A[i].adj(); // partial detA w.r.t A[i]
	}
	// ---------------------------------------------------------
	return true;
}
</pre></font></code>


<hr/>Input File: speed/sacado/det_lu.cpp

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