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LSST Applications g070148d5b3+33e5256705,g0d53e28543+25c8b88941,g0da5cf3356+2dd1178308,g1081da9e2a+62d12e78cb,g17e5ecfddb+7e422d6136,g1c76d35bf8+ede3a706f7,g295839609d+225697d880,g2e2c1a68ba+cc1f6f037e,g2ffcdf413f+853cd4dcde,g38293774b4+62d12e78cb,g3b44f30a73+d953f1ac34,g48ccf36440+885b902d19,g4b2f1765b6+7dedbde6d2,g5320a0a9f6+0c5d6105b6,g56b687f8c9+ede3a706f7,g5c4744a4d9+ef6ac23297,g5ffd174ac0+0c5d6105b6,g6075d09f38+66af417445,g667d525e37+2ced63db88,g670421136f+2ced63db88,g71f27ac40c+2ced63db88,g774830318a+463cbe8d1f,g7876bc68e5+1d137996f1,g7985c39107+62d12e78cb,g7fdac2220c+0fd8241c05,g96f01af41f+368e6903a7,g9ca82378b8+2ced63db88,g9d27549199+ef6ac23297,gabe93b2c52+e3573e3735,gb065e2a02a+3dfbe639da,gbc3249ced9+0c5d6105b6,gbec6a3398f+0c5d6105b6,gc9534b9d65+35b9f25267,gd01420fc67+0c5d6105b6,geee7ff78d7+a14128c129,gf63283c776+ede3a706f7,gfed783d017+0c5d6105b6,w.2022.47
LSST Data Management Base Package
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Namespaces | |
| namespace | cmodel |
| namespace | common |
| namespace | detail |
| namespace | optimizer |
| namespace | pixelFitRegion |
| namespace | priors |
| namespace | psf |
| namespace | version |
Classes | |
| class | AdaptiveImportanceSampler |
| Sampler class that performs Monte Carlo sampling, while iteratively updating the analytic distribution from which points are drawn. More... | |
| class | CModelAlgorithm |
| Main public interface class for CModel algorithm. More... | |
| struct | CModelControl |
| The main control object for CModel, containing parameters for the final linear fit and aggregating the other control objects. More... | |
| struct | CModelResult |
| Master result object for CModel, containing results for the final linear fit and three nested CModelStageResult objects for the results of the previous stages. More... | |
| struct | CModelStageControl |
| Nested control object for CModel that configures one of the three ("initial", "exp", "dev") nonlinear fitting stages. More... | |
| struct | CModelStageResult |
| Result object for a single nonlinear fitting stage of the CModel algorithm. More... | |
| class | DoubleShapeletPsfApproxAlgorithm |
| An algorithm that fits a 2-component shapelet approximation to the PSF model. More... | |
| class | DoubleShapeletPsfApproxControl |
| Control object used to configure a 2-shapelet fit to a PSF model; see DoubleShapeletPsfApproxAlgorithm. More... | |
| class | EpochFootprint |
| An image at one epoch of a galaxy, plus associated info. More... | |
| class | GeneralPsfFitter |
| Class for fitting multishapelet models to PSF images. More... | |
| class | GeneralPsfFitterAlgorithm |
| class | GeneralPsfFitterComponentControl |
| Control object used to define one piece of multishapelet fit to a PSF model; see GeneralPsfFitterControl. More... | |
| class | GeneralPsfFitterControl |
| Control object used to configure a multishapelet fit to a PSF model; see GeneralPsfFitter. More... | |
| class | ImportanceSamplerControl |
| Control object for one iteration of adaptive importance sampling. More... | |
| class | Likelihood |
| Base class for optimizer/sampler likelihood functions that compute likelihood at a point. More... | |
| struct | LocalUnitTransform |
| A local mapping between two UnitSystems. More... | |
| class | Mixture |
| class | MixtureComponent |
| A weighted Student's T or Gaussian distribution used as a component in a Mixture. More... | |
| class | MixturePrior |
| A prior that's flat in amplitude parameters, and uses a Mixture for nonlinear parameters. More... | |
| class | MixtureUpdateRestriction |
| Helper class used to define restrictions to the form of the component parameters in Mixture::updateEM. More... | |
| class | Model |
| Abstract base class and concrete factories that define multi-shapelet galaxy models. More... | |
| class | MultiModel |
| A concrete Model class that simply concatenates several other Models. More... | |
| class | MultiShapeletPsfLikelihood |
| Likelihood object used to fit multishapelet models to PSF model images; mostly for internal use by GeneralPsfFitter. More... | |
| class | Optimizer |
| A numerical optimizer customized for least-squares problems with Bayesian priors. More... | |
| class | OptimizerControl |
| Configuration object for Optimizer. More... | |
| class | OptimizerHistoryRecorder |
| class | OptimizerObjective |
| Base class for objective functions for Optimizer. More... | |
| class | PixelFitRegion |
| struct | PixelFitRegionControl |
| class | Prior |
| Base class for Bayesian priors. More... | |
| class | Sampler |
| class | SamplingObjective |
| class | SemiEmpiricalPrior |
| A piecewise prior motivated by both real distributions and practical considerations. More... | |
| struct | SemiEmpiricalPriorControl |
| class | SoftenedLinearPrior |
| A prior that's linear in radius and flat in ellipticity, with a cubic roll-off at the edges. More... | |
| struct | SoftenedLinearPriorControl |
| class | TruncatedGaussian |
| Represents a multidimensional Gaussian function truncated at zero. More... | |
| class | TruncatedGaussianEvaluator |
| Helper class for evaluating the -log of a TruncatedGaussian. More... | |
| class | TruncatedGaussianLogEvaluator |
| Helper class for evaluating the -log of a TruncatedGaussian. More... | |
| class | TruncatedGaussianSampler |
| Helper class for drawing samples from a TruncatedGaussian. More... | |
| struct | UnitSystem |
| A simple struct that combines a Wcs and a PhotoCalib. More... | |
| class | UnitTransformedLikelihood |
| A concrete Likelihood class that does not require its parameters and data to be in the same UnitSystem. More... | |
| class | UnitTransformedLikelihoodControl |
| Control object used to initialize a UnitTransformedLikelihood. More... | |
Typedefs | |
| typedef float | Pixel |
| Typedefs to be used for pixel values. More... | |
| typedef double | Scalar |
| Typedefs to be used for probability and parameter values. More... | |
| typedef Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > | Matrix |
| typedef Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > | Vector |
| typedef afw::table::Key< Scalar > | ScalarKey |
| typedef afw::table::Key< afw::table::Array< Scalar > > | ArrayKey |
| typedef std::vector< std::shared_ptr< Model > > | ModelVector |
Functions | |
| void | solveTrustRegion (ndarray::Array< Scalar, 1, 1 > const &x, ndarray::Array< Scalar const, 2, 1 > const &F, ndarray::Array< Scalar const, 1, 1 > const &g, double r, double tolerance) |
| Solve a symmetric quadratic matrix equation with a ball constraint. More... | |
| typedef Eigen::Matrix<Scalar,Eigen::Dynamic,Eigen::Dynamic> lsst::meas::modelfit::Matrix |
| typedef float lsst::meas::modelfit::Pixel |
| typedef double lsst::meas::modelfit::Scalar |
| typedef Eigen::Matrix<Scalar,Eigen::Dynamic,1> lsst::meas::modelfit::Vector |
| void lsst::meas::modelfit::solveTrustRegion | ( | ndarray::Array< Scalar, 1, 1 > const & | x, |
| ndarray::Array< Scalar const, 2, 1 > const & | F, | ||
| ndarray::Array< Scalar const, 1, 1 > const & | g, | ||
| double | r, | ||
| double | tolerance | ||
| ) |
Solve a symmetric quadratic matrix equation with a ball constraint.
This computes a near-exact solution to the "trust region subproblem" necessary in trust-region-based nonlinear optimizers:
\[ \min_x{\quad g^T x + \frac{1}{2}x^T F x}\quad\quad\quad \text{s.t.} ||x|| \le r \]
The tolerance parameter sets how close to \(r\) we require the norm of the solution to be when it lies on the constraint, as a fraction of \(r\) itself.
This implementation is based on the algorithm described in Section 4.3 of "Nonlinear Optimization" by Nocedal and Wright.
1.9.5