# Estimation Methods Tab¶

In the **Est:Methods** tab a user configures all the algorithms involved
in fitting the parameters. There are 4 of them as illustrated below. In the current
implementation there is only one option for the method in 3 of the cases, although more may be added later. In all 4
algorithms a user may adjust some control parameters.

## Objective Function¶

In the top left corner is the **Objective Function** which compares the results of one simulation performed by the optimizer
with the data table mapping as defined in the data tab. There are options to normalize the data for
each column, and use the log of the values. If both options are
selected, the log of the values is selected first, then the columns
are normalized. All the data values must be strictly positive if the
log option is checked, as the log of 0 or of a negative value is
undefined. In addition a user can select to use the absolute value of
the difference between the observed and the computed data instead of
the square of the difference when computing the objective function.

## Solver¶

In the bottom left corner is the **solver** that will be used by the optimizer to generate the values that will be
compared with the datatable at each iteration. Adjusting the control parameter values in this tab will have no effect on the parameter values
that are assigned in the Sim:Methods tab.

## Confidence Interval¶

In the top right corner is the *confidence interval* method which computes *p-values*, *t-statistics* and some other
measures using the *Fisher* information matrix. This is currently the only available method and it is crude.

## Options for the Optimizer¶

In the bottom right corner the user configures the **Optimizer**. There are 2 optimization methods available:

Adrian DE, an implementation of the Differential Evolution method, which was developed by Rainer Storn, and described in

R. Storn, “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces,” J. Global Optim. 11(4), 341-359 (Dec. 1997).

DEDiscover uses the implementation by Adrian Michel available on github

Trust Region, an implementation of the interior-point method of Coleman and Li, described in:

T.F. Coleman and Y. Li, An interior trust region approach for nonlinear minimization subject to bounds”, SIAM J. Opt. 6(1996) 418-445.

This method is likely to be of not much use for most problems.