Prev Next Index-> contents reference index search external Up-> CppAD ADFun sparsity_pattern ForSparseJac for_sparse_jac.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 ForSparseJac-> for_sparse_jac.cpp for_sparse_jac.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 Jacobian Sparsity: Example and Test
 # include <set> # include <cppad/cppad.hpp> namespace { // ------------------------------------------------------------- // define the template function BoolCases<Vector> template <typename Vector> // vector class, elements of type bool bool BoolCases(void) { bool ok = true; using CppAD::AD; // domain space vector size_t n = 2; CPPAD_TESTVECTOR(AD<double>) X(n); X[0] = 0.; X[1] = 1.; // declare independent variables and start recording CppAD::Independent(X); // range space vector size_t m = 3; CPPAD_TESTVECTOR(AD<double>) Y(m); Y[0] = X[0]; Y[1] = X[0] * X[1]; Y[2] = X[1]; // create f: X -> Y and stop tape recording CppAD::ADFun<double> f(X, Y); // sparsity pattern for the identity matrix Vector r(n * n); size_t i, j; for(i = 0; i < n; i++) { for(j = 0; j < n; j++) r[ i * n + j ] = (i == j); } // sparsity pattern for F'(x) Vector s(m * n); s = f.ForSparseJac(n, r); // check values ok &= (s[ 0 * n + 0 ] == true); // Y[0] does depend on X[0] ok &= (s[ 0 * n + 1 ] == false); // Y[0] does not depend on X[1] ok &= (s[ 1 * n + 0 ] == true); // Y[1] does depend on X[0] ok &= (s[ 1 * n + 1 ] == true); // Y[1] does depend on X[1] ok &= (s[ 2 * n + 0 ] == false); // Y[2] does not depend on X[0] ok &= (s[ 2 * n + 1 ] == true); // Y[2] does depend on X[1] // check that values are stored ok &= (f.size_forward_bool() > 0); ok &= (f.size_forward_set() == 0); // sparsity pattern for F'(x)^T, note R is the identity, so R^T = R bool transpose = true; Vector st(n * m); st = f.ForSparseJac(n, r, transpose); // check values ok &= (st[ 0 * m + 0 ] == true); // Y[0] does depend on X[0] ok &= (st[ 1 * m + 0 ] == false); // Y[0] does not depend on X[1] ok &= (st[ 0 * m + 1 ] == true); // Y[1] does depend on X[0] ok &= (st[ 1 * m + 1 ] == true); // Y[1] does depend on X[1] ok &= (st[ 0 * m + 2 ] == false); // Y[2] does not depend on X[0] ok &= (st[ 1 * m + 2 ] == true); // Y[2] does depend on X[1] // check that values are stored ok &= (f.size_forward_bool() > 0); ok &= (f.size_forward_set() == 0); // free values from forward calculation f.size_forward_bool(0); ok &= (f.size_forward_bool() == 0); return ok; } // define the template function SetCases<Vector> template <typename Vector> // vector class, elements of type std::set<size_t> bool SetCases(void) { bool ok = true; using CppAD::AD; // domain space vector size_t n = 2; CPPAD_TESTVECTOR(AD<double>) X(n); X[0] = 0.; X[1] = 1.; // declare independent variables and start recording CppAD::Independent(X); // range space vector size_t m = 3; CPPAD_TESTVECTOR(AD<double>) Y(m); Y[0] = X[0]; Y[1] = X[0] * X[1]; Y[2] = X[1]; // create f: X -> Y and stop tape recording CppAD::ADFun<double> f(X, Y); // sparsity pattern for the identity matrix Vector r(n); size_t i; for(i = 0; i < n; i++) { assert( r[i].empty() ); r[i].insert(i); } // sparsity pattern for F'(x) Vector s(m); s = f.ForSparseJac(n, r); // an interator to a standard set std::set<size_t>::iterator itr; bool found; // Y[0] does depend on X[0] found = s[0].find(0) != s[0].end(); ok &= ( found == true ); // Y[0] does not depend on X[1] found = s[0].find(1) != s[0].end(); ok &= ( found == false ); // Y[1] does depend on X[0] found = s[1].find(0) != s[1].end(); ok &= ( found == true ); // Y[1] does depend on X[1] found = s[1].find(1) != s[1].end(); ok &= ( found == true ); // Y[2] does not depend on X[0] found = s[2].find(0) != s[2].end(); ok &= ( found == false ); // Y[2] does depend on X[1] found = s[2].find(1) != s[2].end(); ok &= ( found == true ); // check that values are stored ok &= (f.size_forward_set() > 0); ok &= (f.size_forward_bool() == 0); // sparsity pattern for F'(x)^T bool transpose = true; Vector st(n); st = f.ForSparseJac(n, r, transpose); // Y[0] does depend on X[0] found = st[0].find(0) != st[0].end(); ok &= ( found == true ); // Y[0] does not depend on X[1] found = st[1].find(0) != st[1].end(); ok &= ( found == false ); // Y[1] does depend on X[0] found = st[0].find(1) != st[0].end(); ok &= ( found == true ); // Y[1] does depend on X[1] found = st[1].find(1) != st[1].end(); ok &= ( found == true ); // Y[2] does not depend on X[0] found = st[0].find(2) != st[0].end(); ok &= ( found == false ); // Y[2] does depend on X[1] found = st[1].find(2) != st[1].end(); ok &= ( found == true ); // check that values are stored ok &= (f.size_forward_set() > 0); ok &= (f.size_forward_bool() == 0); return ok; } } // End empty namespace # include <vector> # include <valarray> bool ForSparseJac(void) { bool ok = true; // Run with Vector equal to four different cases // all of which are Simple Vectors with elements of type bool. ok &= BoolCases< CppAD::vectorBool >(); ok &= BoolCases< CppAD::vector <bool> >(); ok &= BoolCases< std::vector <bool> >(); ok &= BoolCases< std::valarray <bool> >(); // Run with Vector equal to two different cases both of which are // Simple Vectors with elements of type std::set<size_t> typedef std::set<size_t> set; ok &= SetCases< CppAD::vector <set> >(); // ok &= SetCases< std::vector <set> >(); // Do not use valarray because its element access in the const case // returns a copy instead of a reference // ok &= SetCases< std::valarray <set> >(); return ok; } 
Input File: example/sparse/for_sparse_jac.cpp