Ocean::Geometry::NonLinearUniversalOptimizationSparse Class Reference
This class implements optimizations for universal sparse problems. More...
Inheritance diagram for Ocean::Geometry::NonLinearUniversalOptimizationSparse:
Data Structures | |
| class | IndividualModelsIndividualModels |
| This class implements an optimization for universal sparse problems with two types of individual models (optimization problems). More... | |
| class | SharedModelIndividualModels |
| This class implements an optimization for universal sparse problems with one shared model (optimization problem) and concurrently several individual models (optimization problems). More... | |
| class | SharedModelIndividualModelsIndividualModels |
| This class implements an optimization for universal sparse problems with one common shared model (optimization problem) and two types of individual models (optimization problems). More... | |
Additional Inherited Members | |
 Static Protected Member Functions inherited from Ocean::Geometry::NonLinearOptimization | |
| template<typename T > | |
| static bool | denseOptimization (T &provider, const unsigned int iterations=5u, const Estimator::EstimatorType estimator=Estimator::ET_SQUARE, Scalar lambda=Scalar(0.001), const Scalar lambdaFactor=Scalar(5), Scalar *initialError=nullptr, Scalar *finalError=nullptr, const Matrix *invertedCovariances=nullptr, Scalars *intermediateErrors=nullptr) |
| Invokes the optimization of a dense (matrix) optimization problem. More... | |
| template<typename T , Estimator::EstimatorType tEstimator> | |
| static bool | denseOptimization (T &provider, const unsigned int iterations=5u, Scalar lambda=Scalar(0.001), const Scalar lambdaFactor=Scalar(5), Scalar *initialError=nullptr, Scalar *finalError=nullptr, const Matrix *invertedCovariances=nullptr, Scalars *intermediateErrors=nullptr) |
| Invokes the optimization of a dense (matrix) optimization problem. More... | |
| template<typename T > | |
| static bool | sparseOptimization (T &provider, const unsigned int iterations=5u, const Estimator::EstimatorType estimator=Estimator::ET_SQUARE, Scalar lambda=Scalar(0.001), const Scalar lambdaFactor=Scalar(5), Scalar *initialError=nullptr, Scalar *finalError=nullptr, const Matrix *invertedCovariances=nullptr, Scalars *intermediateErrors=nullptr) |
| Invokes the optimization of a sparse (matrix) optimization problem. More... | |
| template<typename T , Estimator::EstimatorType tEstimator> | |
| static bool | sparseOptimization (T &provider, const unsigned int iterations=5u, Scalar lambda=Scalar(0.001), const Scalar lambdaFactor=Scalar(5), Scalar *initialError=nullptr, Scalar *finalError=nullptr, const Matrix *invertedCovariances=nullptr, Scalars *intermediateErrors=nullptr) |
| Invokes the optimization of a sparse (matrix) optimization problem. More... | |
| template<typename T > | |
| static bool | advancedDenseOptimization (T &advancedDenseProvider, const unsigned int iterations, Scalar lambda, const Scalar lambdaFactor, Scalar *initialError=nullptr, Scalar *finalError=nullptr, Scalars *intermediateErrors=nullptr) |
| Invokes the optimization of a dense (matrix) optimization problem using an advanced optimization provider. More... | |
| template<typename T > | |
| static bool | advancedSparseOptimization (T &advancedSparseProvider, const unsigned int iterations, Scalar lambda, const Scalar lambdaFactor, Scalar *initialError=nullptr, Scalar *finalError=nullptr, Scalars *intermediateErrors=nullptr) |
| Invokes the optimization of a sparse (matrix) optimization problem using an advanced optimization provider. More... | |
| template<Estimator::EstimatorType tEstimator> | |
| static Scalar | sqrErrors2robustErrors2 (const Scalars &sqrErrors, const size_t modelParameters, Vector2 *weightedErrors, Vector2 *weightVectors, const SquareMatrix2 *transposedInvertedCovariances) |
| Translates the n/2 squared errors that correspond to n elements in the error vector to robust errors. More... | |
| template<Estimator::EstimatorType tEstimator, size_t tDimension> | |
| static Scalar | sqrErrors2robustErrors (const Scalars &sqrErrors, const size_t modelParameters, StaticBuffer< Scalar, tDimension > *weightedErrors, StaticBuffer< Scalar, tDimension > *weightVectors, const Matrix *transposedInvertedCovariances) |
| Translates the n/i squared errors that correspond to n elements in the error vector to robust errors. More... | |
| template<Estimator::EstimatorType tEstimator> | |
| static Scalar | sqrErrors2robustErrors_i (const Scalars &sqrErrors, const size_t modelParameters, const size_t dimension, Scalar *weightedErrors_i, Scalar *weightVectors_i, const Matrix *transposedInvertedCovariances_i) |
| Translates the n/i squared errors that correspond to n elements in the error vector to robust errors. More... | |
| static Scalar | sqrErrors2robustErrors2 (const Estimator::EstimatorType estimator, const Scalars &sqrErrors, const size_t modelParameters, Vector2 *weightedErrors, Vector2 *weightVectors, const SquareMatrix2 *transposedInvertedCovariances) |
| Translates the n/2 squared errors that correspond to n elements in the error vector to robust errors. More... | |
| template<size_t tDimension> | |
| static Scalar | sqrErrors2robustErrors (const Estimator::EstimatorType estimator, const Scalars &sqrErrors, const size_t modelParameters, StaticBuffer< Scalar, tDimension > *weightedErrors, StaticBuffer< Scalar, tDimension > *weightVectors, const Matrix *transposedInvertedCovariances) |
| Translates the n/i squared errors that correspond to n elements in the error vector to robust errors. More... | |
| static Scalar | sqrErrors2robustErrors_i (const Estimator::EstimatorType estimator, const Scalars &sqrErrors, const size_t modelParameters, const size_t dimension, Scalar *weightedErrors_i, Scalar *weightVectors_i, const Matrix *transposedInvertedCovariances_i) |
| Translates the n/i squared errors that correspond to n elements in the error vector to robust errors. More... | |
Detailed Description
This class implements optimizations for universal sparse problems.
The documentation for this class was generated from the following file:
