Class sym::Linearizer#
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template<typename ScalarType>
class Linearizer# Class for evaluating multiple Factors at the linearization point given by a Values.
Stores the original Factors as well as the LinearizedFactors, and provides tools for aggregating keys and building a large jacobian / hessian for optimization.
For efficiency, prefer calling Relinearize() instead of re-constructing this object!
Public Functions
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Linearizer(const std::string &name, const std::vector<Factor<Scalar>> &factors, const std::vector<Key> &key_order = {}, bool include_jacobians = false, bool debug_checks = false)#
Construct a Linearizer from factors and optional keys
- Parameters:
factors – Only stores a pointer, MUST be in scope for the lifetime of this object!
key_order – If provided, acts as an ordered set of keys that form the state vector to optimize. Can equal the set of all factor keys or a subset of all factor keys. If not provided, it is computed from all keys for all factors using a default ordering.
debug_checks – Whether to perform additional sanity checks for NaNs. This uses additional compute but not additional memory except for logging.
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void Relinearize(const Values<Scalar> &values, SparseLinearization<Scalar> &linearization)#
Update linearization at a new evaluation point
This is more efficient than reconstructing this object repeatedly. On the first call, it will allocate memory and perform analysis needed for efficient repeated relinearization.
TODO(aaron): This should be const except that it can initialize the object
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bool IsInitialized() const#
Whether this contains values, versus having not been evaluated yet
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const std::vector<LinearizedSparseFactor> &LinearizedSparseFactors() const#
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Linearizer(const std::string &name, const std::vector<Factor<Scalar>> &factors, const std::vector<Key> &key_order = {}, bool include_jacobians = false, bool debug_checks = false)#