This class implements non linear optimization algorithms for coordinate transformations.  
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| static bool  | optimizeObjectTransformation (const AnyCamera &camera, const HomogenousMatrices4 &world_T_cameras, const HomogenousMatrix4 &world_T_object, const ObjectPointGroups &objectPointGroups, const ImagePointGroups &imagePointGroups, HomogenousMatrix4 &optimized_world_T_object, const unsigned int iterations=20u, 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) | 
|   | Minimizes the projection error for several 3D object points projected into several camera images via a 6-DOF object transformation (to be optimized).  
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| static bool  | optimizeObjectTransformationIF (const AnyCamera &camera, const HomogenousMatrices4 &flippedCameras_T_world, const HomogenousMatrix4 &world_T_object, const ObjectPointGroups &objectPointGroups, const ImagePointGroups &imagePointGroups, HomogenousMatrix4 &optimized_world_T_object, const unsigned int iterations=20u, 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) | 
|   | Minimizes the projection error for several 3D object points projected into several camera images via a 6-DOF object transformation (to be optimized).  
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| static bool  | optimizeObjectTransformationStereo (const AnyCamera &cameraA, const AnyCamera &cameraB, const HomogenousMatrices4 &world_T_camerasA, const HomogenousMatrices4 &world_T_camerasB, const HomogenousMatrix4 &world_T_object, const ObjectPointGroups &objectPointGroupsA, const ObjectPointGroups &objectPointGroupsB, const ImagePointGroups &imagePointGroupsA, const ImagePointGroups &imagePointGroupsB, HomogenousMatrix4 &optimized_world_T_object, const unsigned int iterations=20u, 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) | 
|   | Minimizes the projection error for several 3D object points projected into several stereo camera images via a 6-DOF object transformation (to be optimized).  
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| static bool  | optimizeObjectTransformationStereoIF (const AnyCamera &cameraA, const AnyCamera &cameraB, const HomogenousMatrices4 &flippedCamerasA_T_world, const HomogenousMatrices4 &flippedCamerasB_T_world, const HomogenousMatrix4 &world_T_object, const ObjectPointGroups &objectPointGroupsA, const ObjectPointGroups &objectPointGroupsB, const ImagePointGroups &imagePointGroupsA, const ImagePointGroups &imagePointGroupsB, HomogenousMatrix4 &optimized_world_T_object, const unsigned int iterations=20u, 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) | 
|   | Minimizes the projection error for several 3D object points projected into several stereo camera images via a 6-DOF object transformation (to be optimized).  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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| 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.  
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This class implements non linear optimization algorithms for coordinate transformations.