Prev Next Index-> contents reference index search external Up-> CppAD ADFun sparsity_pattern for_hes_sparsity for_hes_sparsity.cpp ADFun-> record_adfun drivers Forward Reverse sparsity_pattern sparse_derivative optimize abs_normal FunCheck check_for_nan sparsity_pattern-> for_jac_sparsity ForSparseJac rev_jac_sparsity RevSparseJac rev_hes_sparsity RevSparseHes for_hes_sparsity ForSparseHes dependency.cpp rc_sparsity.cpp subgraph_sparsity for_hes_sparsity-> for_hes_sparsity.cpp for_hes_sparsity.cpp Headings

$\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }$
Forward Mode Hessian Sparsity: Example and Test
# include <cppad/cppad.hpp> bool for_hes_sparsity(void) { bool ok = true; using CppAD::AD; typedef CPPAD_TESTVECTOR(size_t) SizeVector; typedef CppAD::sparse_rc<SizeVector> sparsity; // // domain space vector size_t n = 3; CPPAD_TESTVECTOR(AD<double>) ax(n); ax[0] = 0.; ax[1] = 1.; ax[2] = 2.; // declare independent variables and start recording CppAD::Independent(ax); // range space vector size_t m = 2; CPPAD_TESTVECTOR(AD<double>) ay(m); ay[0] = sin( ax[2] ); ay[1] = ax[0] * ax[1]; // create f: x -> y and stop tape recording CppAD::ADFun<double> f(ax, ay); // include all x components in sparsity pattern CPPAD_TESTVECTOR(bool) select_domain(n); for(size_t j = 0; j < n; j++) select_domain[j] = true; // compute sparsity pattern for H(x) = F_1''(x) CPPAD_TESTVECTOR(bool) select_range(m); select_range[0] = false; select_range[1] = true; bool internal_bool = true; sparsity pattern_out; f.for_hes_sparsity( select_domain, select_range, internal_bool, pattern_out ); size_t nnz = pattern_out.nnz(); ok &= nnz == 2; ok &= pattern_out.nr() == n; ok &= pattern_out.nc() == n; { // check results const SizeVector& row( pattern_out.row() ); const SizeVector& col( pattern_out.col() ); SizeVector row_major = pattern_out.row_major(); // ok &= row[ row_major[0] ] == 0 && col[ row_major[0] ] == 1; ok &= row[ row_major[1] ] == 1 && col[ row_major[1] ] == 0; } // // compute sparsity pattern for H(x) = F_0''(x) select_range[0] = true; select_range[1] = false; f.for_hes_sparsity( select_domain, select_range, internal_bool, pattern_out ); nnz = pattern_out.nnz(); ok &= nnz == 1; ok &= pattern_out.nr() == n; ok &= pattern_out.nc() == n; { // check results const SizeVector& row( pattern_out.row() ); const SizeVector& col( pattern_out.col() ); // ok &= row[0] == 2 && col[0] == 2; } return ok; } 
Input File: example/sparse/for_hes_sparsity.cpp