# Changeset 1525

Ignore:
Timestamp:
Sep 22, 2009 6:25:12 AM (11 years ago)
Message:

trunk: Change description of connection to description of vector of sets.

Location:
Files:
9 edited

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 r1524 \param hes_sparsity The connections in \a hes_sparsity for the from node with index \a i_z are the Hessian sparsity pattern for the function G The set with index \a i_z in \a hes_sparsity is the Hessian sparsity pattern for the function G where one of the partials is with respect to z. \n \n If y_2 is a variable, the connections in \a hes_sparsity for the from node with index \a arg are the Hessian sparsity pattern the set with index \a arg in \a hes_sparsity is the Hessian sparsity pattern where one of the partials is with respect to y_2. On input, this pattern corresponds to the function G. \n If y_3 is a variable, the connections in \a hes_sparsity for the from node with index \a arg are the Hessian sparsity pattern the set with index \a arg in \a hes_sparsity is the Hessian sparsity pattern where one of the partials is with respect to y_3. On input, this pattern corresponds to the function G.

 r1524 Reverse mode Hessian sparsity operations for LdpOp and LdvOp This routine is given the (EDIT THIS) connections corresponding to This routine is given the sparsity patterns for G(z , v[x] , w , u ... ) and it uses them to compute the (EDIT THIS) connections corresponding to and it uses them to compute the sparsity patterns for \verbatim H( v[x] , w , u , ... ) = G[ z( v[x] ) , v[x] , w , u , ... ]

 r1524 \b Output: For j = 1 , ... , \a n, the reverse Hessian sparsity pattern for the independent dependent variable with index (j-1) is given by the from connections (EDIT THIS) for the node with index j with index (j-1) is given by the set with index j in \a rev_hes_sparse. The values in the rest of \a rev_hes_sparse are not specified; i.e., CPPAD_ASSERT_UNKNOWN( numvar > 0 ); // number of to nodes in (EDIT THIS) connections // upper limit exclusive for set elements size_t limit   = rev_hes_sparse.limit(); CPPAD_ASSERT_UNKNOWN( rev_hes_sparse.limit() == limit );