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Improved documentation for elimination functions

release/4.3a0
Richard Roberts 2011-09-23 02:34:07 +00:00
parent 019892b900
commit d127abfd17
1 changed files with 98 additions and 0 deletions
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98
gtsam/linear/GaussianFactorGraph.h
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@ -182,26 +182,124 @@ namespace gtsam {
/**
* Combine and eliminate several factors.
* \ingroup LinearSolving
*/
JacobianFactor::shared_ptr CombineJacobians(
const FactorGraph<JacobianFactor>& factors,
const VariableSlots& variableSlots);
/**
* Densely combine and partially eliminate JacobianFactors to produce a
* single conditional with nrFrontals frontal variables and a remaining
* factor.
* Variables are eliminated in the natural order of the variable indices of in the factors.
* @param factors Factors to combine and eliminate
* @param nrFrontals Number of frontal variables to eliminate.
* @return The conditional and remaining factor
* \ingroup LinearSolving
*/
GaussianFactorGraph::EliminationResult EliminateJacobians(const FactorGraph<
JacobianFactor>& factors, size_t nrFrontals = 1);
/**
* Densely partially eliminate with QR factorization. HessianFactors are
* first factored with Cholesky decomposition to produce JacobianFactors,
* by calling the conversion constructor JacobianFactor(const HessianFactor&).
* Variables are eliminated in the natural order of the variable indices of in
* the factors.
* @param factors Factors to combine and eliminate
* @param nrFrontals Number of frontal variables to eliminate.
* @return The conditional and remaining factor
* \ingroup LinearSolving
*/
GaussianFactorGraph::EliminationResult EliminateQR(const FactorGraph<
GaussianFactor>& factors, size_t nrFrontals = 1);
/**
* Densely partially eliminate with Cholesky factorization. JacobianFactors
* are left-multiplied with their transpose to form the Hessian using the
* conversion constructor HessianFactor(const JacobianFactor&).
*
* If any factors contain constrained noise models (any sigmas equal to
* zero), QR factorization will be performed instead, because our current
* implementation cannot handle constrained noise models in Cholesky
* factorization. EliminateCholesky(), on the other hand, will fail if any
* factors contain constrained noise models.
*
* Variables are eliminated in the natural order of the variable indices of in
* the factors.
* @param factors Factors to combine and eliminate
* @param nrFrontals Number of frontal variables to eliminate.
* @return The conditional and remaining factor
* \ingroup LinearSolving
*/
GaussianFactorGraph::EliminationResult EliminatePreferCholesky(const FactorGraph<
GaussianFactor>& factors, size_t nrFrontals = 1);
/**
* Densely partially eliminate with Cholesky factorization. JacobianFactors
* are left-multiplied 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 natural order of the variable indices of in
* the factors.
* @param factors Factors to combine and eliminate
* @param nrFrontals Number of frontal variables to eliminate.
* @return The conditional and remaining factor
* \ingroup LinearSolving
*/
GaussianFactorGraph::EliminationResult EliminateCholesky(const FactorGraph<
GaussianFactor>& factors, size_t nrFrontals = 1);
/**
* Densely partially eliminate with LDL factorization. JacobianFactors
* are left-multiplied with their transpose to form the Hessian using the
* conversion constructor HessianFactor(const JacobianFactor&).
*
* If any factors contain constrained noise models (any sigmas equal to
* zero), QR factorization will be performed instead, because our current
* implementation cannot handle constrained noise models in LDL
* factorization. EliminateLDL(), on the other hand, will fail if any
* factors contain constrained noise models.
*
* Variables are eliminated in the natural order of the variable indices of in
* the factors.
* @param factors Factors to combine and eliminate
* @param nrFrontals Number of frontal variables to eliminate.
* @return The conditional and remaining factor
* \ingroup LinearSolving
*/
GaussianFactorGraph::EliminationResult EliminatePreferLDL(const FactorGraph<
GaussianFactor>& factors, size_t nrFrontals = 1);
/**
* Densely partially eliminate with LDL factorization. JacobianFactors
* are left-multiplied 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 LDL factorization. The function EliminatePreferLDL()
* automatically does QR instead when this is the case.
*
* Variables are eliminated in the natural order of the variable indices of in
* the factors.
* @param factors Factors to combine and eliminate
* @param nrFrontals Number of frontal variables to eliminate.
* @return The conditional and remaining factor
* \ingroup LinearSolving
*/
GaussianFactorGraph::EliminationResult EliminateLDL(const FactorGraph<
GaussianFactor>& factors, size_t nrFrontals = 1);