\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)

atomic_four_lin_ode_set.hpp

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atomic_lin_ode Set Routine: Example Implementation

Syntax

call_id = lin_ode . set ( r , step , pattern , transpose )

Prototype

    typedef CppAD::sparse_rc< CppAD::vector<size_t> > sparse_rc;

template <class Base>
size_t atomic_lin_ode<Base>::set(
    const Base& r, const Base& step, sparse_rc& pattern, const bool& transpose
)

Purpose

Stores the auxiliary information for a an atomic operation that computes the solution of a linear ODE.

r

This argument is the final value for the variable that the ODE is with respect to.

step

This is a positive maximum step size to use when solving the ODE.

pattern

This argument is a sparsity pattern. It would be const except for the fact that pattern.set_row_major () is called so that checking for equality is faster; see set_row_major .

transpose

If this argument is true (false) the sparsity pattern is for \(A(x)\R{T}\) (\(A(x)\)).

Source

# include <cppad/example/atomic_four/lin_ode/lin_ode.hpp>

namespace CppAD { // BEGIN_CPPAD_NAMESPACE
// BEGIN PROTOTYPE
template <class Base>
size_t atomic_lin_ode<Base>::set(
    const Base& r, const Base& step, sparse_rc& pattern, const bool& transpose
)
// END PROTOTYPE
{
    // pattern
    // set_row_major so that checking for pattern equality is faster
    pattern.set_row_major();
    //
    // thread
    size_t thread = thread_alloc::thread_num();
    //
    // work_[thread]
    if( work_[thread] == nullptr )
        work_[thread] = new thread_struct;
    //
    // pattern_vec
    CppAD::vector<sparse_rc>& pattern_vec( work_[thread]->pattern_vec );
    //
    // pattern_index
    size_t     n_pattern     = pattern_vec.size();
    size_t     pattern_index = n_pattern;
    for(size_t i = 0; i < n_pattern; ++i)
        if( pattern == pattern_vec[i] )
            pattern_index = i;
    if( pattern_index == n_pattern )
    {   pattern_vec.push_back( pattern );
        CPPAD_ASSERT_UNKNOWN( pattern_vec[pattern_index] == pattern );
    }
    //
    // call_vec
    CppAD::vector<call_struct>& call_vec( work_[thread]->call_vec );
    //
    // call_id
    size_t call_id = call_vec.size();
    //
    // call
    call_struct call;
    call.thread        = thread;
    call.r             = r;
    call.step          = step;
    call.pattern_index = pattern_index;
    call.transpose     = transpose;
    //
    // work_[thread]
    call_vec.push_back( call );
    //
    return call_id;
}
} // END_CPPAD_NAMESPACE