void ForSparseJacSet(
bool transpose ,
size_t q ,
const VectorSet& r ,
VectorSet& s ,
size_t total_num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play ,
CPPAD_INTERNAL_SPARSE_SET& for_jac_sparsity )
{
// temporary indices
size_t i, j;
std::set<size_t>::const_iterator itr;
// range and domain dimensions for F
size_t m = dep_taddr.size();
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
q > 0,
"RevSparseJac: q is not greater than zero"
);
CPPAD_ASSERT_KNOWN(
size_t(r.size()) == n || transpose,
"RevSparseJac: size of r is not equal to n and transpose is false."
);
CPPAD_ASSERT_KNOWN(
size_t(r.size()) == q || ! transpose,
"RevSparseJac: size of r is not equal to q and transpose is true."
);
// allocate memory for the requested sparsity calculation
for_jac_sparsity.resize(total_num_var, q);
// set values corresponding to independent variables
if( transpose )
{ for(i = 0; i < q; i++)
{ // add the elements that are present
itr = r[i].begin();
while( itr != r[i].end() )
{ j = *itr++;
CPPAD_ASSERT_KNOWN(
j < n,
"ForSparseJac: transpose is true and element of the set\n"
"r[j] has value greater than or equal n."
);
CPPAD_ASSERT_UNKNOWN( ind_taddr[j] < total_num_var );
// operator for j-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
for_jac_sparsity.add_element( ind_taddr[j], i);
}
}
}
else
{ for(i = 0; i < n; i++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[i] < total_num_var );
// ind_taddr[i] is operator taddr for i-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[i] ) == InvOp );
// add the elements that are present
itr = r[i].begin();
while( itr != r[i].end() )
{ j = *itr++;
CPPAD_ASSERT_KNOWN(
j < q,
"ForSparseJac: an element of the set r[i] "
"has value greater than or equal q."
);
for_jac_sparsity.add_element( ind_taddr[i], j);
}
}
}
// evaluate the sparsity patterns
ForJacSweep(
n,
total_num_var,
&play,
for_jac_sparsity
);
// return values corresponding to dependent variables
CPPAD_ASSERT_UNKNOWN( size_t(s.size()) == m || transpose );
CPPAD_ASSERT_UNKNOWN( size_t(s.size()) == q || ! transpose );
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < total_num_var );
// extract results from for_jac_sparsity
// and add corresponding elements to sets in s
CPPAD_ASSERT_UNKNOWN( for_jac_sparsity.end() == q );
for_jac_sparsity.begin( dep_taddr[i] );
j = for_jac_sparsity.next_element();
while( j < q )
{ if( transpose )
s[j].insert(i);
else s[i].insert(j);
j = for_jac_sparsity.next_element();
}
}
}
void ForSparseJacBool(
bool transpose ,
size_t q ,
const VectorSet& r ,
VectorSet& s ,
size_t total_num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play ,
sparse_pack& for_jac_sparsity )
{
// temporary indices
size_t i, j;
// range and domain dimensions for F
size_t m = dep_taddr.size();
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
q > 0,
"ForSparseJac: q is not greater than zero"
);
CPPAD_ASSERT_KNOWN(
size_t(r.size()) == n * q,
"ForSparseJac: size of r is not equal to\n"
"q times domain dimension for ADFun object."
);
// allocate memory for the requested sparsity calculation result
for_jac_sparsity.resize(total_num_var, q);
// set values corresponding to independent variables
for(i = 0; i < n; i++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[i] < total_num_var );
// ind_taddr[i] is operator taddr for i-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[i] ) == InvOp );
// set bits that are true
if( transpose )
{ for(j = 0; j < q; j++) if( r[ j * n + i ] )
for_jac_sparsity.add_element( ind_taddr[i], j);
}
else
{ for(j = 0; j < q; j++) if( r[ i * q + j ] )
for_jac_sparsity.add_element( ind_taddr[i], j);
}
}
// evaluate the sparsity patterns
ForJacSweep(
n,
total_num_var,
&play,
for_jac_sparsity
);
// return values corresponding to dependent variables
CPPAD_ASSERT_UNKNOWN( size_t(s.size()) == m * q );
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < total_num_var );
// extract the result from for_jac_sparsity
if( transpose )
{ for(j = 0; j < q; j++)
s[ j * m + i ] = false;
}
else
{ for(j = 0; j < q; j++)
s[ i * q + j ] = false;
}
CPPAD_ASSERT_UNKNOWN( for_jac_sparsity.end() == q );
for_jac_sparsity.begin( dep_taddr[i] );
j = for_jac_sparsity.next_element();
while( j < q )
{ if( transpose )
s[j * m + i] = true;
else s[i * q + j] = true;
j = for_jac_sparsity.next_element();
}
}
}
void RevSparseHesSet(
bool transpose ,
size_t q ,
const VectorSet& s ,
VectorSet& h ,
size_t num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play ,
CPPAD_INTERNAL_SPARSE_SET& for_jac_sparsity )
{
// temporary indices
size_t i, j;
std::set<size_t>::const_iterator itr;
// check VectorSet is Simple Vector class with sets for elements
CheckSimpleVector<std::set<size_t>, VectorSet>(
one_element_std_set<size_t>(), two_element_std_set<size_t>()
);
// range and domain dimensions for F
# ifndef NDEBUG
size_t m = dep_taddr.size();
# endif
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
q == for_jac_sparsity.end(),
"RevSparseHes: q is not equal to its value\n"
"in the previous call to ForSparseJac with this ADFun object."
);
CPPAD_ASSERT_KNOWN(
s.size() == 1,
"RevSparseHes: size of s is not equal to one."
);
// Array that will hold reverse Jacobian dependency flag.
// Initialize as true for the dependent variables.
pod_vector<bool> RevJac;
RevJac.extend(num_var);
for(i = 0; i < num_var; i++)
RevJac[i] = false;
itr = s[0].begin();
while( itr != s[0].end() )
{ i = *itr++;
CPPAD_ASSERT_KNOWN(
i < m,
"RevSparseHes: an element of the set s[0] has value "
"greater than or equal m"
);
CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < num_var );
RevJac[ dep_taddr[i] ] = true;
}
// vector of sets that will hold reverse Hessain values
CPPAD_INTERNAL_SPARSE_SET rev_hes_sparsity;
rev_hes_sparsity.resize(num_var, q);
// compute the Hessian sparsity patterns
RevHesSweep(
n,
num_var,
&play,
for_jac_sparsity,
RevJac.data(),
rev_hes_sparsity
);
// return values corresponding to independent variables
// j is index corresponding to reverse mode partial
CPPAD_ASSERT_UNKNOWN( size_t(h.size()) == q || transpose );
CPPAD_ASSERT_UNKNOWN( size_t(h.size()) == n || ! transpose );
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[j] < num_var );
CPPAD_ASSERT_UNKNOWN( ind_taddr[j] == j + 1 );
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
// extract the result from rev_hes_sparsity
// and add corresponding elements to result sets in h
CPPAD_ASSERT_UNKNOWN( rev_hes_sparsity.end() == q );
rev_hes_sparsity.begin(j+1);
i = rev_hes_sparsity.next_element();
while( i < q )
{ if( transpose )
h[j].insert(i);
else h[i].insert(j);
i = rev_hes_sparsity.next_element();
}
}
return;
}
void RevSparseHesBool(
bool transpose ,
size_t q ,
const VectorSet& s ,
VectorSet& h ,
size_t num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play ,
sparse_pack& for_jac_sparsity )
{
// temporary indices
size_t i, j;
// check Vector is Simple VectorSet class with bool elements
CheckSimpleVector<bool, VectorSet>();
// range and domain dimensions for F
size_t m = dep_taddr.size();
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
q == for_jac_sparsity.end(),
"RevSparseHes: q is not equal to its value\n"
"in the previous call to ForSparseJac with this ADFun object."
);
CPPAD_ASSERT_KNOWN(
size_t(s.size()) == m,
"RevSparseHes: size of s is not equal to\n"
"range dimension for ADFun object."
);
// Array that will hold reverse Jacobian dependency flag.
// Initialize as true for the dependent variables.
pod_vector<bool> RevJac;
RevJac.extend(num_var);
for(i = 0; i < num_var; i++)
RevJac[i] = false;
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < num_var );
RevJac[ dep_taddr[i] ] = s[i];
}
// vector of sets that will hold reverse Hessain values
sparse_pack rev_hes_sparsity;
rev_hes_sparsity.resize(num_var, q);
// compute the Hessian sparsity patterns
RevHesSweep(
n,
num_var,
&play,
for_jac_sparsity,
RevJac.data(),
rev_hes_sparsity
);
// return values corresponding to independent variables
CPPAD_ASSERT_UNKNOWN( size_t(h.size()) == n * q );
for(j = 0; j < n; j++)
{ for(i = 0; i < q; i++)
{ if( transpose )
h[ j * q + i ] = false;
else h[ i * n + j ] = false;
}
}
// j is index corresponding to reverse mode partial
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[j] < num_var );
// ind_taddr[j] is operator taddr for j-th independent variable
CPPAD_ASSERT_UNKNOWN( ind_taddr[j] == j + 1 );
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
// extract the result from rev_hes_sparsity
CPPAD_ASSERT_UNKNOWN( rev_hes_sparsity.end() == q );
rev_hes_sparsity.begin(j + 1);
i = rev_hes_sparsity.next_element();
while( i < q )
{ if( transpose )
h[ j * q + i ] = true;
else h[ i * n + j ] = true;
i = rev_hes_sparsity.next_element();
}
}
return;
}
void RevSparseJacSet(
size_t p ,
const VectorSet& s ,
VectorSet& r ,
size_t total_num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play )
{
// temporary indices
size_t i, j;
std::set<size_t>::const_iterator itr;
// check VectorSet is Simple Vector class with sets for elements
static std::set<size_t> two, three;
if( two.empty() )
{ two.insert(2);
three.insert(3);
}
CPPAD_ASSERT_UNKNOWN( two.size() == 1 );
CPPAD_ASSERT_UNKNOWN( three.size() == 1 );
CheckSimpleVector<std::set<size_t>, VectorSet>(two, three);
// range and domain dimensions for F
size_t m = dep_taddr.size();
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
p > 0,
"RevSparseJac: p (first argument) is not greater than zero"
);
CPPAD_ASSERT_KNOWN(
s.size() == p,
"RevSparseJac: s (second argument) length is not equal to "
"p (first argument)."
);
// vector of sets that will hold the results
sparse_set var_sparsity;
var_sparsity.resize(total_num_var, p);
// The sparsity pattern corresponding to the dependent variables
for(i = 0; i < p; i++)
{ itr = s[i].begin();
while(itr != s[i].end())
{ j = *itr++;
CPPAD_ASSERT_KNOWN(
j < m,
"RevSparseJac: an element of the set s[i] "
"has value greater than or equal m."
);
CPPAD_ASSERT_UNKNOWN( dep_taddr[j] < total_num_var );
var_sparsity.add_element( dep_taddr[j], i );
}
}
// evaluate the sparsity patterns
RevJacSweep(
n,
total_num_var,
&play,
var_sparsity
);
// return values corresponding to dependent variables
CPPAD_ASSERT_UNKNOWN( r.size() == p );
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[j] == (j+1) );
// ind_taddr[j] is operator taddr for j-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
// extract result from rev_hes_sparsity
// and add corresponding elements to sets in r
CPPAD_ASSERT_UNKNOWN( var_sparsity.end() == p );
var_sparsity.begin(j+1);
i = var_sparsity.next_element();
while( i < p )
{ r[i].insert(j);
i = var_sparsity.next_element();
}
}
}
void RevSparseJacBool(
size_t p ,
const VectorSet& s ,
VectorSet& r ,
size_t total_num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play )
{
// temporary indices
size_t i, j;
// check VectorSet is Simple Vector class with bool elements
CheckSimpleVector<bool, VectorSet>();
// range and domain dimensions for F
size_t m = dep_taddr.size();
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
p > 0,
"RevSparseJac: p (first argument) is not greater than zero"
);
CPPAD_ASSERT_KNOWN(
s.size() == p * m,
"RevSparseJac: s (second argument) length is not equal to\n"
"p (first argument) times range dimension for ADFun object."
);
// vector of sets that will hold the results
sparse_pack var_sparsity;
var_sparsity.resize(total_num_var, p);
// The sparsity pattern corresponding to the dependent variables
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < total_num_var );
for(j = 0; j < p; j++) if( s[ i * m + j ] )
var_sparsity.add_element( dep_taddr[i], j );
}
// evaluate the sparsity patterns
RevJacSweep(
n,
total_num_var,
&play,
var_sparsity
);
// return values corresponding to dependent variables
CPPAD_ASSERT_UNKNOWN( r.size() == p * n );
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[j] == (j+1) );
// ind_taddr[j] is operator taddr for j-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
// extract the result from var_sparsity
for(i = 0; i < p; i++)
r[ i * n + j ] = false;
CPPAD_ASSERT_UNKNOWN( var_sparsity.end() == p );
var_sparsity.begin(j+1);
i = var_sparsity.next_element();
while( i < p )
{ r[ i * n + j ] = true;
i = var_sparsity.next_element();
}
}
}
void RevSparseJacSet(
bool transpose ,
bool dependency ,
size_t q ,
const VectorSet& r ,
VectorSet& s ,
size_t total_num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play )
{
// temporary indices
size_t i, j;
std::set<size_t>::const_iterator itr;
// check VectorSet is Simple Vector class with sets for elements
CheckSimpleVector<std::set<size_t>, VectorSet>(
one_element_std_set<size_t>(), two_element_std_set<size_t>()
);
// domain dimensions for F
size_t n = ind_taddr.size();
size_t m = dep_taddr.size();
CPPAD_ASSERT_KNOWN(
q > 0,
"RevSparseJac: q is not greater than zero"
);
CPPAD_ASSERT_KNOWN(
size_t(r.size()) == q || transpose,
"RevSparseJac: size of r is not equal to q and transpose is false."
);
CPPAD_ASSERT_KNOWN(
size_t(r.size()) == m || ! transpose,
"RevSparseJac: size of r is not equal to m and transpose is true."
);
// vector of lists that will hold the results
CPPAD_INTERNAL_SPARSE_SET var_sparsity;
var_sparsity.resize(total_num_var, q);
// The sparsity pattern corresponding to the dependent variables
if( transpose )
{ for(i = 0; i < m; i++)
{ itr = r[i].begin();
while(itr != r[i].end())
{ j = *itr++;
CPPAD_ASSERT_KNOWN(
j < q,
"RevSparseJac: transpose is true and element of the set\n"
"r[i] has value greater than or equal q."
);
CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < total_num_var );
var_sparsity.add_element( dep_taddr[i], j );
}
}
}
else
{ for(i = 0; i < q; i++)
{ itr = r[i].begin();
while(itr != r[i].end())
{ j = *itr++;
CPPAD_ASSERT_KNOWN(
j < m,
"RevSparseJac: transpose is false and element of the set\n"
"r[i] has value greater than or equal range dimension."
);
CPPAD_ASSERT_UNKNOWN( dep_taddr[j] < total_num_var );
var_sparsity.add_element( dep_taddr[j], i );
}
}
}
// evaluate the sparsity patterns
RevJacSweep(
dependency,
n,
total_num_var,
&play,
var_sparsity
);
// return values corresponding to dependent variables
CPPAD_ASSERT_UNKNOWN( size_t(s.size()) == q || transpose );
CPPAD_ASSERT_UNKNOWN( size_t(s.size()) == n || ! transpose );
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[j] == (j+1) );
// ind_taddr[j] is operator taddr for j-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
CPPAD_ASSERT_UNKNOWN( var_sparsity.end() == q );
var_sparsity.begin(j+1);
i = var_sparsity.next_element();
while( i < q )
{ if( transpose )
s[j].insert(i);
else s[i].insert(j);
i = var_sparsity.next_element();
}
}
}
void RevSparseJacBool(
bool transpose ,
bool dependency ,
size_t q ,
const VectorSet& r ,
VectorSet& s ,
size_t total_num_var ,
CppAD::vector<size_t>& dep_taddr ,
CppAD::vector<size_t>& ind_taddr ,
CppAD::player<Base>& play )
{
// temporary indices
size_t i, j;
// check VectorSet is Simple Vector class with bool elements
CheckSimpleVector<bool, VectorSet>();
// range and domain dimensions for F
size_t m = dep_taddr.size();
size_t n = ind_taddr.size();
CPPAD_ASSERT_KNOWN(
q > 0,
"RevSparseJac: q is not greater than zero"
);
CPPAD_ASSERT_KNOWN(
size_t(r.size()) == q * m,
"RevSparseJac: size of r is not equal to\n"
"q times range dimension for ADFun object."
);
// vector of sets that will hold the results
sparse_pack var_sparsity;
var_sparsity.resize(total_num_var, q);
// The sparsity pattern corresponding to the dependent variables
for(i = 0; i < m; i++)
{ CPPAD_ASSERT_UNKNOWN( dep_taddr[i] < total_num_var );
if( transpose )
{ for(j = 0; j < q; j++) if( r[ j * m + i ] )
var_sparsity.add_element( dep_taddr[i], j );
}
else
{ for(j = 0; j < q; j++) if( r[ i * q + j ] )
var_sparsity.add_element( dep_taddr[i], j );
}
}
// evaluate the sparsity patterns
RevJacSweep(
dependency,
n,
total_num_var,
&play,
var_sparsity
);
// return values corresponding to dependent variables
CPPAD_ASSERT_UNKNOWN( size_t(s.size()) == q * n );
for(j = 0; j < n; j++)
{ CPPAD_ASSERT_UNKNOWN( ind_taddr[j] == (j+1) );
// ind_taddr[j] is operator taddr for j-th independent variable
CPPAD_ASSERT_UNKNOWN( play.GetOp( ind_taddr[j] ) == InvOp );
// extract the result from var_sparsity
if( transpose )
{ for(i = 0; i < q; i++)
s[ j * q + i ] = false;
}
else
{ for(i = 0; i < q; i++)
s[ i * n + j ] = false;
}
CPPAD_ASSERT_UNKNOWN( var_sparsity.end() == q );
var_sparsity.begin(j+1);
i = var_sparsity.next_element();
while( i < q )
{ if( transpose )
s[ j * q + i ] = true;
else s[ i * n + j ] = true;
i = var_sparsity.next_element();
}
}
}
