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<center><b><big><big>Adolc Speed: Matrix Multiplication</big></big></b></center>
<br/>
<b><big><a name="Specifications" id="Specifications">Specifications</a></big></b>
<br/>
See <a href="link_mat_mul.xml" target="_top"><span style='white-space: nowrap'>link_mat_mul</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;adolc/adolc.h&gt;
# include &lt;cppad/vector.hpp&gt;
# include &lt;cppad/speed/mat_sum_sq.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;
extern bool global_boolsparsity;

bool link_mat_mul(
	size_t                           size     , 
	size_t                           repeat   , 
	CppAD::vector&lt;double&gt;&amp;           x        ,
	CppAD::vector&lt;double&gt;&amp;           z        ,
	CppAD::vector&lt;double&gt;&amp;           dz       )
{
	// speed test global option values
	if( global_boolsparsity )
		return false; 
	if( global_memory || global_atomic || global_optimize )
		return false;
	// -----------------------------------------------------
	// setup
	typedef adouble    ADScalar;
	typedef ADScalar*  ADVector;

	int tag  = 0;         // tape identifier
	int m    = 1;         // number of dependent variables
	int n    = size*size; // number of independent variables
	double f;             // function value
	int j;                // temporary index

	// set up for thread_alloc memory allocator (fast and checks for leaks)
	using CppAD::thread_alloc; // the allocator
	size_t capacity;           // capacity of an allocation

	// AD domain space vector
	ADVector X = thread_alloc::create_array&lt;ADScalar&gt;(size_t(n), capacity);

	// Product matrix
	ADVector Y = thread_alloc::create_array&lt;ADScalar&gt;(size_t(n), capacity);

	// AD range space vector
	ADVector Z = thread_alloc::create_array&lt;ADScalar&gt;(size_t(m), capacity);

	// vector with matrix value
	double* mat = thread_alloc::create_array&lt;double&gt;(size_t(n), capacity);

	// vector of reverse mode weights
	double* u  = thread_alloc::create_array&lt;double&gt;(size_t(m), capacity);
	u[0] = 1.;

	// gradient
	double* grad = thread_alloc::create_array&lt;double&gt;(size_t(n), capacity);

	// ----------------------------------------------------------------------
	if( ! global_onetape ) while(repeat--)
	{	// choose a matrix
		CppAD::uniform_01(n, mat);

		// declare independent variables
		int keep = 1; // keep forward mode results
		trace_on(tag, keep);
		for(j = 0; j &lt; n; j++)
			X[j] &lt;&lt;= mat[j];

		// do computations
		CppAD::mat_sum_sq(size, X, Y, Z);

		// create function object f : X -&gt; Z
		Z[0] &gt;&gt;= f;
		trace_off();

		// evaluate and return gradient using reverse mode
		fos_reverse(tag, m, n, u, grad);
	}
	else
	{	// choose a matrix
		CppAD::uniform_01(n, mat);

		// declare independent variables
		int keep = 0; // do not keep forward mode results
		trace_on(tag, keep);
		for(j = 0; j &lt; n; j++)
			X[j] &lt;&lt;= mat[j];

		// do computations
		CppAD::mat_sum_sq(size, X, Y, Z);

		// create function object f : X -&gt; Z
		Z[0] &gt;&gt;= f;
		trace_off();

		while(repeat--)
		{	// choose a matrix
			CppAD::uniform_01(n, mat);

			// evaluate the determinant at the new matrix value
			keep = 1; // keep this forward mode result
			zos_forward(tag, m, n, keep, mat, &amp;f); 

			// evaluate and return gradient using reverse mode
			fos_reverse(tag, m, n, u, grad);
		}
	}
	// return function, matrix, and gradient
	z[0] = f;
	for(j = 0; j &lt; n; j++)
	{	x[j]  = mat[j];
		dz[j] = grad[j];
	}

	// tear down
	thread_alloc::delete_array(X);
	thread_alloc::delete_array(Y);
	thread_alloc::delete_array(Z);
	thread_alloc::delete_array(mat);
	thread_alloc::delete_array(u);
	thread_alloc::delete_array(grad);

	return true;
}


</pre></font></code>


<hr/>Input File: speed/adolc/mat_mul.cpp

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