Templating still used in cpp file for generic-ness, but not exposed
anymore
std::tuple has the same performance as Eigen::Triplet, but boost::tuple
is slower. Sparse matrix indices are int instead of size_t for
efficiency (e.g. A(i, j) = s -> i/j are int's instead of size_t's)
compiles and passes tests, but some potentially code-breaking changes in:
Marginals.h - order of arguments had to change since `factorization` has a default value
EliminatableFactorGraph.h - marginalMultifrontalBayesNet and marginalMultifrontalBayesTree no longer accept `boost::none` as a placeholder to specify later arguments
Notes:
EliminateableFactorGraph.h - `orderingType` is no longer needed in function overloads that specify ordering, but I left it for the time being to avoid potential code breaking
This should make merging in develop easier, and it also helps me understand what changed.
I mostly avoided conflicts by keeping Duy's versions of:
Conflicts:
gtsam/3rdparty/metis-5.1.0/CMakeLists.txt
gtsam/linear/JacobianFactor-inl.h
gtsam/linear/NoiseModel.cpp
gtsam/nonlinear/NonlinearFactor.h
and a number of other files. In particular, I did not upgrade Eigen or remove metis.
The following unit tests fail in this branch:
The following tests FAILED:
2 - testWrap (Failed)
85 - testGeneralSFMFactor (SEGFAULT)
142 - testIMUSystem (Failed)
178 - testTSAMFactors (Failed)
Instead of turning Hessian factors into Jacobian factors -- so that they can be eliminated with constrained Jacobian factors using the special QR in Constrained's noise model -- we combine all Hessian factors, eliminate the variable first to have a conditional and a new factor 1, then combine the constrained Jacobians with this conditional (also a Jacobian) to eliminate again, producing the final conditional, and a new factor 2. The two new factors are then combined into a new Hessian factor to be returned.
Currently, when eliminating a constrained variable, EliminatePreferCholesky converts every other factors to JacobianFactor before doing the special QR factorization for constrained variables. Unfortunately, after a constrained nonlinear graph is linearized, new hessian factors from constraints, multiplied with the dual variable (-lambda*\hessian{c} terms in the Lagrangian objective function), might become negative definite, thus cannot be converted to JacobianFactors.
Following EliminateCholesky, this version of EliminatePreferCholesky for constrained var gathers all unconstrained factors into a big joint HessianFactor before converting it into a JacobianFactor to be eliminiated by QR together with the other constrained factors.
Of course, this might not solve the non-positive-definite problem entirely, because (1) the original hessian factors might be non-positive definite and (2) large strange value of lambdas might cause the joint factor non-positive definite [is this true?]. But at least, this will help in typical cases.
Varun Agrawal
Gerry Chen
Fan Jiang
Gerry Chen
Frank Dellaert
Abe
yao
Frank
dellaert
Mike Bosse
Sungtae An
thduynguyen
krunalchande
cbeall3
Yong-Dian Jian
Zsolt Kira
hchiu
Luca
Alex Cunningham
Pablo Fernandez Alcantarilla