Compounds | |
| struct | Gan_LevMarqObs |
| Observation structure for Levenberg-Marquardt minimisation. More... | |
| struct | Gan_LevMarqStruct |
| Structure for holding state of Levenberg-Marquardt algorithm. More... | |
Typedefs | |
| typedef Gan_Bool(* | Gan_LevMarqObsFunc_h )(Gan_Vector *x, Gan_Vector *z, void *zdata, Gan_Vector *h, Gan_Matrix *H) |
| Observation function for standard h-type observations. | |
| typedef Gan_Bool(* | Gan_LevMarqObsFunc_F )(Gan_Vector *x, Gan_Vector *z, void *zdata, Gan_Vector *F, Gan_Matrix *Hx, Gan_Matrix *Hz) |
| Observation function for F-type observations. | |
| typedef Gan_LevMarqObs | Gan_LevMarqObs |
| Observation structure for Levenberg-Marquardt minimisation. | |
| typedef Gan_LevMarqStruct | Gan_LevMarqStruct |
| Structure for holding state of Levenberg-Marquardt algorithm. | |
| typedef Gan_Bool(* | Gan_LevMarqInitFunc )(Gan_Vector *x, Gan_List *obs_list, void *data) |
| Callback function for initialising Levenberg-Marquardt algorithm. | |
Enumerations | |
| enum | Gan_LevMarqObsType { GAN_LEV_MARQ_OBS_H, GAN_LEV_MARQ_OBS_H_ROBUST, GAN_LEV_MARQ_OBS_F } |
| Observation type for Levenberg-Marquardt minimisation. More... | |
Functions | |
| Gan_LevMarqStruct * | gan_lev_marq_form (Gan_LevMarqStruct *lm) |
| Forms a Levenberg-Marquardt structure. | |
| Gan_LevMarqObs * | gan_lev_marq_obs_h (Gan_LevMarqStruct *lm, Gan_Vector *z, void *zdata, Gan_SquMatrix *Ni, Gan_LevMarqObsFunc_h obs_func) |
| Passes an observation to a Levenberg-Marquardt structure. | |
| Gan_LevMarqObs * | gan_lev_marq_obs_h_robust (Gan_LevMarqStruct *lm, Gan_Vector *z, void *zdata, Gan_SquMatrix *Ni, Gan_LevMarqObsFunc_h obs_func, double var_scale, double chi2) |
| Passes a robust observation to a Levenberg-Marquardt structure. | |
| Gan_LevMarqObs * | gan_lev_marq_obs_F (Gan_LevMarqStruct *lm, Gan_Vector *z, void *zdata, Gan_SquMatrix *Ni, Gan_LevMarqObsFunc_F obs_func) |
| Passes an observation to a Levenberg-Marquardt structure. | |
| Gan_Bool | gan_lev_marq_init (Gan_LevMarqStruct *lm, Gan_LevMarqInitFunc init_func, void *data, double *residualp) |
| Initialise Levenberg-Marquardt algorithm. | |
| Gan_Bool | gan_lev_marq_iteration (Gan_LevMarqStruct *lm, double lambda, double *residualp) |
| Applies Levenberg-Marquardt iteration. | |
| Gan_Vector * | gan_lev_marq_get_x (Gan_LevMarqStruct *lm) |
| Returns state of Levenberg-Marquardt minimisation. | |
| Gan_SquMatrix * | gan_lev_marq_get_P (Gan_LevMarqStruct *lm) |
| Returns state covariance of Levenberg-Marquardt minimisation. | |
| void | gan_lev_marq_free (Gan_LevMarqStruct *lm) |
| Frees a Levenberg-Marquardt structure. | |
| Gan_LevMarqStruct * | gan_lev_marq_alloc (void) |
| Macro: Allocates a Levenberg-Marquardt structure. | |
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Observation structure for Levenberg-Marquardt minimisation.
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Observation function for F-type observations. Observation function for F-type observations
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Observation function for standard h-type observations. Observation function for standard h-type observations
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Structure for holding state of Levenberg-Marquardt algorithm.
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Observation type for Levenberg-Marquardt minimisation.
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Macro: Allocates a Levenberg-Marquardt structure. Allocates a structure for computing Levenberg-Marquardt optimisation.
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Forms a Levenberg-Marquardt structure.
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Frees a Levenberg-Marquardt structure.
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Returns state covariance of Levenberg-Marquardt minimisation.
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Returns state of Levenberg-Marquardt minimisation.
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Initialise Levenberg-Marquardt algorithm.
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Applies Levenberg-Marquardt iteration.
if the residual has been reduced from the existing value.
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Passes an observation to a Levenberg-Marquardt structure.
where is the observation vector, is the observation function, is the state vector and is a zero-mean noise vector, with covariance .
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Passes an observation to a Levenberg-Marquardt structure.
where is the observation vector, is the observation function, is the state vector and is a zero-mean noise vector, with covariance .
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Passes a robust observation to a Levenberg-Marquardt structure.
where is the observation vector, is the observation function, is the state vector and is a zero-mean noise vector, with covariance .
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1.3-rc1