gtsam 4.1.1
gtsam

Densely partially eliminate with Cholesky factorization. More...
Modules  
Solving by multifrontal variable elimination (QR and Cholesky)  
An EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net.  
Solving by sequential variable elimination (QR and Cholesky)  
Densely partially eliminate with Cholesky factorization.
Densely partially eliminate with QR factorization, this is usually provided as an argument to one of the factor graph elimination functions (see EliminateableFactorGraph).
JacobianFactors are leftmultiplied with their transpose to form the Hessian using the conversion constructor HessianFactor(const JacobianFactor&).
If any factors contain constrained noise models, this function will fail because our current implementation cannot handle constrained noise models in Cholesky factorization. The function EliminatePreferCholesky() automatically does QR instead when this is the case.
Variables are eliminated in the order specified in keys
.
factors  Factors to combine and eliminate 
keys  The variables to eliminate and their elimination ordering 
JacobianFactors are leftmultiplied with their transpose to form the Hessian using the conversion constructor HessianFactor(const JacobianFactor&).
This function will fall back on QR factorization for any cliques containing JacobianFactor's with constrained noise models.
Variables are eliminated in the order specified in keys
.
factors  Factors to combine and eliminate 
keys  The variables to eliminate and their elimination ordering 
HessianFactors are first factored with Cholesky decomposition to produce JacobianFactors, by calling the conversion constructor JacobianFactor(const HessianFactor&). Variables are eliminated in the order specified in keys
.
factors  Factors to combine and eliminate 
keys  The variables to eliminate in the order as specified here in keys 