C++ VectorSet::end方法代码示例

void color_general_cppad(
	const VectorSet&        pattern ,
	const VectorSize&       row     ,
	const VectorSize&       col     ,
	CppAD::vector<size_t>&  color   )
{	size_t i, j, k, ell, r;

	size_t K = row.size();
	size_t m = pattern.n_set();
	size_t n = pattern.end();

	CPPAD_ASSERT_UNKNOWN( size_t( col.size() )   == K );
	CPPAD_ASSERT_UNKNOWN( size_t( color.size() ) == m );

	// We define the set of rows, columns, and pairs that appear
	// by the set ( row[k], col[k] ) for k = 0, ... , K-1.

	// initialize rows that appear
	CppAD::vector<bool> row_appear(m);
	for(i = 0; i < m; i++)
			row_appear[i] = false;

	// rows and columns that appear
	VectorSet c2r_appear, r2c_appear;
	c2r_appear.resize(n, m);
	r2c_appear.resize(m, n);
	for(k = 0;  k < K; k++)
	{	CPPAD_ASSERT_UNKNOWN( pattern.is_element(row[k], col[k]) );
		row_appear[ row[k] ] = true;
		c2r_appear.add_element(col[k], row[k]);
		r2c_appear.add_element(row[k], col[k]);
	}

	// for each column, which rows are non-zero and do not appear
	VectorSet not_appear;
	not_appear.resize(n, m);
	for(i = 0; i < m; i++)
	{	typename VectorSet::const_iterator pattern_itr(pattern, i);
		j = *pattern_itr;
		while( j != pattern.end() )
		{	if( ! c2r_appear.is_element(j , i) )
				not_appear.add_element(j, i);
			j = *(++pattern_itr);
		}
	}

	// initial coloring
	color.resize(m);
	ell = 0;
	for(i = 0; i < m; i++)
	{	if( row_appear[i] )
			color[i] = ell++;
		else	color[i] = m;
	}
	/*
	See GreedyPartialD2Coloring Algorithm Section 3.6.2 of
	Graph Coloring in Optimization Revisited by
	Assefaw Gebremedhin, Fredrik Maane, Alex Pothen

	The algorithm above was modified (by Brad Bell) to take advantage of the
	fact that only the entries (subset of the sparsity pattern) specified by
	row and col need to be computed.
	*/
	CppAD::vector<bool> forbidden(m);
	for(i = 1; i < m; i++) // for each row that appears
	if( color[i] < m )
	{
		// initial all colors as ok for this row
		// (value of forbidden for ell > initial color[i] does not matter)
		for(ell = 0; ell <= color[i]; ell++)
			forbidden[ell] = false;

		// -----------------------------------------------------
		// Forbid colors for which this row would destroy results:
		//
		// for each column that is non-zero for this row
		typename VectorSet::const_iterator pattern_itr(pattern, i);
		j = *pattern_itr;
		while( j != pattern.end() )
		{	// for each row that appears with this column
			typename VectorSet::const_iterator c2r_itr(c2r_appear, j);
			r = *c2r_itr;
			while( r != c2r_appear.end() )
			{	// if this is not the same row, forbid its color
				if( (r < i) & (color[r] < m) )
					forbidden[ color[r] ] = true;
				r = *(++c2r_itr);
			}
			j = *(++pattern_itr);
		}


		// -----------------------------------------------------
		// Forbid colors that destroy results needed for this row.
		//
		// for each column that appears with this row
		typename VectorSet::const_iterator r2c_itr(r2c_appear, i);
		j = *r2c_itr;
		while( j != r2c_appear.end() )
		{	// For each row that is non-zero for this column
//.........这里部分代码省略.........

void color_general_colpack(
	const VectorSet&        pattern ,
	const VectorSize&       row     ,
	const VectorSize&       col     ,
	CppAD::vector<size_t>&  color   )
{	size_t i, j, k;
	size_t m = pattern.n_set();
	size_t n = pattern.end();

	// Determine number of non-zero entries in each row
	CppAD::vector<size_t> n_nonzero(m);
	size_t n_nonzero_total = 0;
	for(i = 0; i < m; i++)
	{	n_nonzero[i] = 0;
		typename VectorSet::const_iterator pattern_itr(pattern, i);
		j = *pattern_itr;
		while( j != pattern.end() )
		{	n_nonzero[i]++;
			j = *(++pattern_itr);
		}
		n_nonzero_total += n_nonzero[i];
	}

	// Allocate memory and fill in Adolc sparsity pattern
	CppAD::vector<unsigned int*> adolc_pattern(m);
	CppAD::vector<unsigned int>  adolc_memory(m + n_nonzero_total);
	size_t i_memory = 0;
	for(i = 0; i < m; i++)
	{	adolc_pattern[i]    = adolc_memory.data() + i_memory;
		CPPAD_ASSERT_KNOWN(
			std::numeric_limits<unsigned int>::max() >= n_nonzero[i],
			"Matrix is too large for colpack"
		);
		adolc_pattern[i][0] = static_cast<unsigned int>( n_nonzero[i] );
		typename VectorSet::const_iterator pattern_itr(pattern, i);
		j = *pattern_itr;
		k = 1;
		while(j != pattern.end() )
		{
			CPPAD_ASSERT_KNOWN(
				std::numeric_limits<unsigned int>::max() >= j,
				"Matrix is too large for colpack"
			);
			adolc_pattern[i][k++] = static_cast<unsigned int>( j );
			j = *(++pattern_itr);
		}
		CPPAD_ASSERT_UNKNOWN( k == 1 + n_nonzero[i] );
		i_memory += k;
	}
	CPPAD_ASSERT_UNKNOWN( i_memory == m + n_nonzero_total );

	// Must use an external routine for this part of the calculation because
	// ColPack/ColPackHeaders.h has as 'using namespace std' at global level.
	cppad_colpack_general(color, m, n, adolc_pattern);

	return;
}

void color_general_colpack(
    VectorSet&        pattern ,
    const VectorSize&       row     ,
    const VectorSize&       col     ,
    CppAD::vector<size_t>&  color   )
{   size_t i, j, k;
    size_t m = pattern.n_set();
    size_t n = pattern.end();

    // Determine number of non-zero entries in each row
    CppAD::vector<size_t> n_nonzero(m);
    size_t n_nonzero_total = 0;
    for(i = 0; i < m; i++)
    {   n_nonzero[i] = 0;
        pattern.begin(i);
        j = pattern.next_element();
        while( j != pattern.end() )
        {   n_nonzero[i]++;
            j = pattern.next_element();
        }
        n_nonzero_total += n_nonzero[i];
    }

    // Allocate memory and fill in Adolc sparsity pattern
    CppAD::vector<unsigned int*> adolc_pattern(m);
    CppAD::vector<unsigned int>  adolc_memory(m + n_nonzero_total);
    size_t i_memory = 0;
    for(i = 0; i < m; i++)
    {   adolc_pattern[i]    = adolc_memory.data() + i_memory;
        adolc_pattern[i][0] = n_nonzero[i];
        pattern.begin(i);
        j = pattern.next_element();
        k = 1;
        while(j != pattern.end() )
        {   adolc_pattern[i][k++] = j;
            j = pattern.next_element();
        }
        CPPAD_ASSERT_UNKNOWN( k == 1 + n_nonzero[i] );
        i_memory += k;
    }
    CPPAD_ASSERT_UNKNOWN( i_memory == m + n_nonzero_total );

    // Must use an external routine for this part of the calculation because
    // ColPack/ColPackHeaders.h has as 'using namespace std' at global level.
    cppad_colpack_general(color, m, n, adolc_pattern);

    return;
}

size_t ADFun<Base>::SparseHessianCompute(
	const VectorBase&           x           ,
	const VectorBase&           w           ,
	      VectorSet&            sparsity    ,
	const VectorSize&           row         ,
	const VectorSize&           col         ,
	      VectorBase&           hes         ,
	      sparse_hessian_work&  work        )
{
	using   CppAD::vectorBool;
	size_t i, k, ell;

	CppAD::vector<size_t>& color(work.color);
	CppAD::vector<size_t>& order(work.order);

	size_t n = Domain();

	// some values
	const Base zero(0);
	const Base one(1);

	// check VectorBase is Simple Vector class with Base type elements
	CheckSimpleVector<Base, VectorBase>();

	CPPAD_ASSERT_UNKNOWN( size_t(x.size()) == n );
	CPPAD_ASSERT_UNKNOWN( color.size() == 0 || color.size() == n );

	// number of components of Hessian that are required
	size_t K = hes.size();
	CPPAD_ASSERT_UNKNOWN( row.size() == K );
	CPPAD_ASSERT_UNKNOWN( col.size() == K );

	// Point at which we are evaluating the Hessian
	Forward(0, x);

	// check for case where nothing (except Forward above) to do
	if( K == 0 )
		return 0;

	// Rows of the Hessian (i below) correspond to the forward mode index
	// and columns (j below) correspond to the reverse mode index.
	if( color.size() == 0 )
	{
		CPPAD_ASSERT_UNKNOWN( sparsity.n_set() ==  n );
		CPPAD_ASSERT_UNKNOWN( sparsity.end() ==  n );

		// execute coloring algorithm
		color.resize(n);
		color_general_cppad(sparsity, row, col, color);

		// put sorting indices in color order
		VectorSize key(K);
		order.resize(K);
		for(k = 0; k < K; k++)
			key[k] = color[ row[k] ];
		index_sort(key, order);

	}
	size_t n_color = 1;
	for(ell = 0; ell < n; ell++) if( color[ell] < n )
		n_color = std::max(n_color, color[ell] + 1);

	// direction vector for calls to forward (rows of the Hessian)
	VectorBase u(n);

	// location for return values from reverse (columns of the Hessian)
	VectorBase ddw(2 * n);

	// initialize the return value
	for(k = 0; k < K; k++)
		hes[k] = zero;

	// loop over colors
	k = 0;
	for(ell = 0; ell < n_color; ell++)
	{	CPPAD_ASSERT_UNKNOWN( color[ row[ order[k] ] ] == ell );

		// combine all rows with this color
		for(i = 0; i < n; i++)
		{	u[i] = zero;
			if( color[i] == ell )
				u[i] = one;
		}
		// call forward mode for all these rows at once
		Forward(1, u);

		// evaluate derivative of w^T * F'(x) * u
		ddw = Reverse(2, w);

		// set the corresponding components of the result
		while( k < K && color[ row[ order[k] ] ] == ell ) 
		{	hes[ order[k] ] = ddw[ col[ order[k] ] * 2 + 1 ];
			k++;
		}
	}
	return n_color;
}

size_t ADFun<Base>::SparseJacobianRev(
	const VectorBase&           x           ,
	      VectorSet&            p           ,
	const VectorSize&           row         ,
	const VectorSize&           col         ,
	      VectorBase&           jac         ,
	      sparse_jacobian_work& work        )
{
	size_t i, k, ell;

	CppAD::vector<size_t>& order(work.order);
	CppAD::vector<size_t>& color(work.color);

	size_t m = Range();
	size_t n = Domain();

	// some values
	const Base zero(0);
	const Base one(1);

	// check VectorBase is Simple Vector class with Base type elements
	CheckSimpleVector<Base, VectorBase>();

	CPPAD_ASSERT_UNKNOWN( size_t(x.size()) == n );
	CPPAD_ASSERT_UNKNOWN (color.size() == m || color.size() == 0 );

	// number of components of Jacobian that are required
	size_t K = size_t(jac.size());
	CPPAD_ASSERT_UNKNOWN( row.size() == K );
	CPPAD_ASSERT_UNKNOWN( col.size() == K );

	// Point at which we are evaluating the Jacobian
	Forward(0, x);

	// check for case where nothing (except Forward above) to do
	if( K == 0 )
		return 0;

	if( color.size() == 0 )
	{
		CPPAD_ASSERT_UNKNOWN( p.n_set() == m );
		CPPAD_ASSERT_UNKNOWN( p.end()   == n );

		// execute the coloring algorithm
		color.resize(m);
		if(	work.color_method == "cppad" )
			color_general_cppad(p, row, col, color);
		else if( work.color_method == "colpack" )
		{
# if CPPAD_HAS_COLPACK
			color_general_colpack(p, row, col, color);
# else
			CPPAD_ASSERT_KNOWN(
				false,
				"SparseJacobianReverse: work.color_method = colpack "
				"and colpack_prefix missing from cmake command line."
			);
# endif
		}
		else CPPAD_ASSERT_KNOWN(
			false,
			"SparseJacobianReverse: work.color_method is not valid."
		);

		// put sorting indices in color order
		VectorSize key(K);
		order.resize(K);
		for(k = 0; k < K; k++)
			key[k] = color[ row[k] ];
		index_sort(key, order);
	}
	size_t n_color = 1;
	for(i = 0; i < m; i++) if( color[i] < m ) 
		n_color = std::max(n_color, color[i] + 1);

	// weighting vector for calls to reverse
	VectorBase w(m);

	// location for return values from Reverse
	VectorBase dw(n);

	// initialize the return value
	for(k = 0; k < K; k++)
		jac[k] = zero;

	// loop over colors
	k = 0;
	for(ell = 0; ell < n_color; ell++)
	{	CPPAD_ASSERT_UNKNOWN( color[ row[ order[k] ] ] == ell );

		// combine all the rows with this color
		for(i = 0; i < m; i++)
		{	w[i] = zero;
			if( color[i] == ell )
				w[i] = one;
		}
		// call reverse mode for all these rows at once
		dw = Reverse(1, w);

		// set the corresponding components of the result
//.........这里部分代码省略.........

size_t ADFun<Base>::SparseJacobianRev(
	const VectorBase&     x           ,
	VectorSet&            p           ,
	VectorBase&           jac         ,
	sparse_jacobian_work& work        )
{
	using   CppAD::vectorBool;
	size_t i, j, k, ell;

	CppAD::vector<size_t>& row(work.user_row);
	CppAD::vector<size_t>& col(work.user_col);
	CppAD::vector<size_t>& sort_row(work.sort_row);
	CppAD::vector<size_t>& color(work.color);

	size_t m = Range();
	size_t n = Domain();

	// some values
	const Base zero(0);
	const Base one(1);

	// check VectorBase is Simple Vector class with Base type elements
	CheckSimpleVector<Base, VectorBase>();

	CPPAD_ASSERT_UNKNOWN( size_t(x.size()) == n );
	CPPAD_ASSERT_UNKNOWN (color.size() == m || color.size() == 0 );

	// number of components of Jacobian that are required
	size_t K = size_t(jac.size());
	CPPAD_ASSERT_UNKNOWN( row.size() == K+1 );
	CPPAD_ASSERT_UNKNOWN( col.size() == K+1 );
	CPPAD_ASSERT_UNKNOWN( row[K] == m );
	CPPAD_ASSERT_UNKNOWN( col[K] == n );

	// Point at which we are evaluating the Jacobian
	Forward(0, x);

	if( color.size() == 0 )
	{	CPPAD_ASSERT_UNKNOWN( p.n_set() ==  m );
		CPPAD_ASSERT_UNKNOWN( p.end() ==  n );

		// rows and columns that are in the returned jacobian
		VectorSet r_used, c_used;
		r_used.resize(n, m);
		c_used.resize(m, n);
		k = 0;
		while( k < K )
		{	CPPAD_ASSERT_UNKNOWN( 
				row[sort_row[k]] < m && col[sort_row[k]] < n 
			);
			CPPAD_ASSERT_UNKNOWN( 
				k == 0 || row[sort_row[k-1]] <= row[sort_row[k]] 
			);
			CPPAD_ASSERT_KNOWN(
				p.is_element(row[sort_row[k]], col[sort_row[k]]) ,
				"SparseJacobianReverse: "
				"an (row, col) pair is not in sparsity pattern."
			);
			r_used.add_element(col[sort_row[k]], row[sort_row[k]]);
			c_used.add_element(row[sort_row[k]], col[sort_row[k]]);
			k++;
		}
	
		// given a column index, which rows are non-zero and not used
		VectorSet not_used;
		not_used.resize(n, m);
		for(i = 0; i < m; i++)
		{	p.begin(i);
			j = p.next_element();
			while( j != p.end() )
			{	if( ! r_used.is_element(j , i) )
					not_used.add_element(j, i);
				j = p.next_element();
			}
		}
	
		// initial coloring
		color.resize(m);
		for(i = 0; i < m; i++)
			color[i] = i;
	
		// See GreedyPartialD2Coloring Algorithm Section 3.6.2 of
		// Graph Coloring in Optimization Revisited by
		// Assefaw Gebremedhin, Fredrik Maane, Alex Pothen
		vectorBool forbidden(m);
		for(i = 1; i < m; i++)
		{
			// initial all colors as ok for this row
			// (value of forbidden for ell > i does not matter)
			for(ell = 0; ell <= i; ell++)
				forbidden[ell] = false;
	
			// -----------------------------------------------------
			// Forbid colors for which this row would destroy results 
			// for each column that is non-zero for this row
			p.begin(i);
			j = p.next_element();
			while( j != p.end() )
			{	// for each row that this column uses
				r_used.begin(j);
//.........这里部分代码省略.........