Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize > Class Template Reference
This class implements an optimization for universal dense problems with one model (optimization problem). More...
Inheritance diagram for Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >:
Data Structures | |
| class | UniversalOptimizationProvider |
| This class implements a dense universal optimization provider for universal models and measurement/data values. More... | |
Public Types | |
| typedef StaticBuffer< Scalar, tModelSize > | Model |
| Definition of a model. More... | |
| typedef StaticBuffer< Scalar, tExternalModelSize > | ExternalModel |
| Definition of an external model. More... | |
| typedef StaticBuffer< Scalar, tResultDimension > | Result |
| Definition of a model result. More... | |
| typedef Callback< void, const ExternalModel &, const size_t, Result & > | ValueCallback |
| Definition of a callback function for dense value calculation. More... | |
| typedef Callback< bool, const ExternalModel &, const size_t, Result & > | ErrorCallback |
| Definition of a callback function for dense error calculation. More... | |
| typedef Callback< void, Model &, ExternalModel & > | ModelTransformationCallback |
| Definition of a dense model transformation function. More... | |
| typedef Callback< void, Model & > | ModelAdjustmentCallback |
| Definition of a dense model adjustment function. More... | |
Static Public Member Functions | |
| static bool | optimizeUniversalModel (const Model &model, const size_t numberElements, const ValueCallback &valueCallback, const ErrorCallback &errorCallback, const ModelTransformationCallback &modelTransformationCallback, const ModelAdjustmentCallback &modelAdjustmentCallback, Model &optimizedModel, 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, Scalars *intermediateErrors=nullptr) |
| Optimizes a universal model by minimizing the error the model produces. 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
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
class Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >
This class implements an optimization for universal dense problems with one model (optimization problem).
The implementation allows to optimize arbitrary (universal) problems with arbitrary dimensions.
- Template Parameters
-
tModelSize Size of the model, the number of model parameters tResultDimension Number of dimensions that result for each element (measurement) after the model has been applied tExternalModelSize Size of the external model, the number of model parameters
Member Typedef Documentation
◆ ErrorCallback
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef Callback<bool, const ExternalModel&, const size_t, Result&> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::ErrorCallback |
Definition of a callback function for dense error calculation.
The first parameter provides the external model that is applied to determine the error.
The second parameter provides the index of the measurement elements that is used to determine the error.
The third parameter receives the determined error.
The return value provides True if both models provide valid information for the measurement element.
◆ ExternalModel
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef StaticBuffer<Scalar, tExternalModelSize> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::ExternalModel |
Definition of an external model.
◆ Model
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef StaticBuffer<Scalar, tModelSize> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::Model |
Definition of a model.
◆ ModelAdjustmentCallback
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef Callback<void, Model&> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::ModelAdjustmentCallback |
Definition of a dense model adjustment function.
The adjustment function allows to modify the internal model (the modification should be tiny e.g., a normalization of a vector which has almost length 1) before the model will be accepted or rejected The first parameter provides the model to be adjusted
◆ ModelTransformationCallback
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef Callback<void, Model&, ExternalModel&> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::ModelTransformationCallback |
Definition of a dense model transformation function.
The transformation function allows to use an external model function for value and error determination while the internal model is used for the internal optimization.
The first parameter provides the internal model.
The second parameter receives the external model.
◆ Result
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef StaticBuffer<Scalar, tResultDimension> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::Result |
Definition of a model result.
◆ ValueCallback
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize = tModelSize>
| typedef Callback<void, const ExternalModel&, const size_t, Result&> Ocean::Geometry::NonLinearUniversalOptimizationDense< tModelSize, tResultDimension, tExternalModelSize >::ValueCallback |
Definition of a callback function for dense value calculation.
The first parameter provides the external model that is applied to determine the value.
The second parameter provides the index of the measurement elements that is used to determine the value.
The third parameter receives the determined value.
Member Function Documentation
◆ optimizeUniversalModel()
template<unsigned int tModelSize, unsigned int tResultDimension, unsigned int tExternalModelSize>
|
static |
Optimizes a universal model by minimizing the error the model produces.
- Parameters
-
model Universal model that will be optimized numberElements Number of elements (measurements) that are used to determine the optimized model valueCallback Callback function that is used to determine the value for an individual element (measurement) by application of the model errorCallback Callback function that is used to determine the error for an individual element (measurement) modelTransformationCallback Optional callback function allowing to transform the internal model into an extern model if intended modelAdjustmentCallback Optional callback function allowing to adjust the internal (already optimized) model before it will be checked on improvements optimizedModel Resulting optimized model iterations Number of iterations to be applied at most, if no convergence can be reached estimator Robust error estimator to be used lambda Initial Levenberg-Marquardt damping value which may be changed after each iteration using the damping factor, with range [0, infinity) lambdaFactor Levenberg-Marquardt damping factor to be applied to the damping value, with range [1, infinity) initialError Optional resulting averaged pixel error for the given initial parameters, in relation to the defined estimator finalError Optional resulting averaged pixel error for the final optimized parameters, in relation to the defined estimator intermediateErrors Optional resulting intermediate (improving) errors
- Returns
- True, if the model could be optimized
The documentation for this class was generated from the following file:
