hgprieto

Syntax >>>> EXECUTABLE: FILE (BEFORE/AFTER) Parent Command >>> PEST Subcommand – Description iTOUGH2 capabilities can be applied to non-TOUGH2 models using the PEST interface. The user-supplied model must be an executable that can be run from a system command prompt. The system command can also be a script of batch file, in which multiple models […]

Syntax >>>> DECPOINT: POINT/NOPOINT Parent Command >>> PEST Subcommand – Description iTOUGH2 capabilities can be applied to non-TOUGH2 models using the PEST interface. By selecting the keyword NOPOINT, the decimal point in the representation of a parameter in the input file is omitted, potentially increasing the accuracy of a parameter value. However, this should be

Syntax >>> PEST Parent Command >> OPTION Subcommand >>>> DECPOINT >>>> EXECUTABLE >>>> INSTRUCTION >>>> PRECISION >>>> TEMPLATE Description This command invokes fourth-level commands to specify files and options for the PEST/JUPITER interface between iTOUGH2, a user-supplied model, and its input and output files. It is used to (1) identify the executable (or script or

Syntax >>> OBJECTIVE (UNSORTED) (: ninval1 (ninval2 (ninval3)) / FILE: filename) Parent Command >> OPTION Subcommand – Description (synonym for command >>> GRID SEARCH) Example (see command >>> GRID SEARCH) See Also >>> GRID SEARCH

Syntax >>> LEVENBERG-MARQUARDT (IDENTITY/EIGENVALUE) (SUPER/TRUNCATE (: (-)trunc)) Parent Command >> OPTION Subcommand – Description This command selects the Levenberg-Marquardt algorithm to minimize the objective function. This is the default minimization algorithm. The Levenberg-Marquardt algorithm combines the robustness of a steepest descent method with the efficiency of a Gauss-Newton step (see command >>> GAUSS-NEWTON): where the

Syntax >>> LEAST-SQUARES Parent Command >> OPTION Subcommand – Description This command selects least-squares optimization, i.e., the objective function to be minimized is the sum of the squared weighted residuals. Minimizing the squared weighted residuals leads to a maximum-likelihood estimate if the errors are normally distributed with zero mean and covariance matrix Czz: Least-squares estimation

Syntax >>> L1-ESTIMATOR Parent Command >> OPTION Subcommand – Description This command selects the L1-estimator, i.e., the objective function to be minimized is the sum of the weighted absolute residuals. Minimizing the mean absolute deviation leads to a maximum-likelihood estimate if the errors follow a double exponential distribution. The L1-estimator should be used, for example,

Syntax >>> GAUSS-NEWTON Parent Command >> OPTION Subcommand – Description This command performs Gauss-Newton steps to minimize the objective function. The Gauss-Newton algorithm assumes linearity and can be described as follows: Gauss-Newton steps are efficient if the model is linear (only one iteration required to find minimum) or nearly-linear. If the model is highly nonlinear,

Syntax >>> GRID SEARCH (UNSORTED) (: ninval1 (ninval2 (ninval3)) / FILE: filename) or >>> OBJECTIVE (UNSORTED) (: ninval1 (ninval2 (ninval3)) / FILE: filename) Parent Command >> OPTION Subcommand – Description This command evaluates the objective function for a number of specific parameter sets. A arbitrarily long list of parameter sets can be provided on file

Syntax >>> DESIGN Parent Command >> OPTION   Subcommand –   Description (synonym for command >>> SENSITIVITY) Example (see command >>> SENSITIVITY)   See Also >>> SENSITIVITY (op)