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Merged in feature/refactoredUpdateATA (pull request #106) 

Re-factored updateATA
release/4.3a0
Frank Dellaert 2015-02-17 13:47:48 +01:00
parent d617462e74 5c5447e7b3
commit f2fabc18c8
1 changed files with 31 additions and 35 deletions
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66
gtsam/linear/HessianFactor.cpp
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@ -433,7 +433,8 @@ void HessianFactor::updateATA(const HessianFactor& update, const Scatter& scatte
}
/* ************************************************************************* */
void HessianFactor::updateATA(const JacobianFactor& update, const Scatter& scatter) {
void HessianFactor::updateATA(const JacobianFactor& update,
const Scatter& scatter) {
// This function updates 'combined' with the information in 'update'.
// 'scatter' maps variables in the update factor to slots in the combined
@ -441,52 +442,47 @@ void HessianFactor::updateATA(const JacobianFactor& update, const Scatter& scatt
gttic(updateATA);
if(update.rows() > 0)
{
// First build an array of slots
gttic(slots);
FastVector<DenseIndex> slots(update.size());
DenseIndex slot = 0;
BOOST_FOREACH(Key j, update) {
slots[slot] = scatter.at(j).slot;
++ slot;
}
gttoc(slots);
if (update.rows() > 0) {
gttic(whiten);
// Whiten the factor if it has a noise model
boost::optional<JacobianFactor> _whitenedFactor;
const JacobianFactor* whitenedFactor;
if(update.get_model())
{
if(update.get_model()->isConstrained())
throw invalid_argument("Cannot update HessianFactor from JacobianFactor with constrained noise model");
const JacobianFactor* whitenedFactor = &update;
if (update.get_model()) {
if (update.get_model()->isConstrained())
throw invalid_argument(
"Cannot update HessianFactor from JacobianFactor with constrained noise model");
_whitenedFactor = update.whiten();
whitenedFactor = &(*_whitenedFactor);
}
else
{
whitenedFactor = &update;
}
gttoc(whiten);
const VerticalBlockMatrix& updateBlocks = whitenedFactor->matrixObject();
// A is the whitened Jacobian matrix A, and we are going to perform I += A'*A below
const VerticalBlockMatrix& A = whitenedFactor->matrixObject();
// N is number of variables in HessianFactor, n in JacobianFactor
DenseIndex N = this->info_.nBlocks() - 1, n = A.nBlocks() - 1;
// First build an array of slots
gttic(slots);
FastVector<DenseIndex> slots(n + 1);
DenseIndex slot = 0;
BOOST_FOREACH(Key j, update)
slots[slot++] = scatter.at(j).slot;
slots[n] = N;
gttoc(slots);
// Apply updates to the upper triangle
gttic(update);
DenseIndex nrInfoBlocks = this->info_.nBlocks(), nrUpdateBlocks = updateBlocks.nBlocks();
for(DenseIndex j2 = 0; j2 < nrUpdateBlocks; ++j2) { // Horizontal block of Hessian
DenseIndex slot2 = (j2 == (DenseIndex)update.size()) ? nrInfoBlocks-1 : slots[j2];
assert(slot2 >= 0 && slot2 <= nrInfoBlocks);
for(DenseIndex j1 = 0; j1 <= j2; ++j1) { // Vertical block of Hessian
DenseIndex slot1 = (j1 == (DenseIndex)update.size()) ? nrInfoBlocks-1 : slots[j1];
assert(slot1 >= 0 && slot1 < nrInfoBlocks);
if(slot1 != slot2)
info_(slot1, slot2).knownOffDiagonal() += updateBlocks(j1).transpose() * updateBlocks(j2);
else
info_(slot1, slot2).selfadjointView().rankUpdate(updateBlocks(j1).transpose());
// Loop over blocks of A, including RHS with j==n
for (DenseIndex j = 0; j <= n; ++j) {
DenseIndex J = slots[j]; // get block in Hessian
// Fill off-diagonal blocks with Ai'*Aj
for (DenseIndex i = 0; i < j; ++i) {
DenseIndex I = slots[i]; // get block in Hessian
info_(I, J).knownOffDiagonal() += A(i).transpose() * A(j);
}
// Fill diagonal block with Aj'*Aj
info_(J, J).selfadjointView().rankUpdate(A(j).transpose());
}
gttoc(update);
}