This function creates or updates a list which can be used to modify
the behaviour of optimization methods. The goal of this function is to manage
options list with a set of fields (for example, 'MaxFunEvals',
'MaxIter', etc...). The user can create a new list with empty fields or
create a new structure with default fields which correspond to a particular
algorithm. The user can also configure each field and set it to a particular
value. Finally, the user passes the list to an optimization function so
that the algorithm uses the options configured by the user.
If provided, the
Additional arguments which would be included in the options output
Most optimization algorithms require many algorithmic parameters such as the
number of iterations or the number of function evaluations. If these
parameters are given to the optimization function as input parameters, this
forces both the user and the developer to manage many input parameters. The
goal of the
optimset function is to simplify the management of input
arguments, by gathering all the parameters into a single list.
While the current implementation of the
optimset function only supports
fminbnd function, it is designed to be
extended to as many optimization function as required. Because all
optimization algorithms do not require the same parameters, the data structure
aims at remaining flexible. But, most of the time, most parameters are the
same from algorithm to algorithm, for example, the tolerance parameters which
drive the termination criteria are often the same, even if the termination
criteria itself is not the same.
Optimization parameters that are returned by the
optimset function and
that can be defined in
... are the following:
The verbose level. The default value is 'notify'. The following is a list of available verbose levels.
The algorithm displays no message at all.
The algorithm displays message if the termination criteria is not reached at the end of the optimization. This may happen if the maximum number or iterations of the maximum number of function evaluations is reached and warns the user of a convergence problem.
The algorithm displays a message at the end of the optimization, showing the number of iterations, the number of function evaluations and the status of the optimization. This option includes the messages generated by the 'notify' option i.e. warns in case of a convergence problem.
The algorithm displays a one-line message at each iteration. This option includes the messages generated by the 'notify' option i.e. warns in case of a convergence problem. It also includes the message generated by the 'final' option.
A logical flag to enable the checking of function values.
The maximum number of evaluations of the cost function.
The maximum number of iterations.
A function which is called at each iteration to print out intermediate state of the optimization algorithm (for example into a log file).
A function which is called at each iteration to plot the intermediate state of the optimization algorithm (for example into a 2D graphic).
The absolute tolerance on function value.
The absolute tolerance on the variable x.
Specific to Box method: the number of consecutive times the
TolFun criteria must be met to terminate the optimization.
Specific to Box method: the parameter used to project the vertices into the bounds in Box's algorithm
Specific to Box method: the scaling coefficient used to scale the trial point for function improvement or into the constraints of Box's algorithm
Specific to Box method: the minimum value of alpha when scaling the vertices of the simplex into nonlinear constraints in Box's algorithm
Output and plot functions The 'OutputFcn' and 'PlotFcns' options accept as argument a function (or a list of functions). In the client optimization algorithm, this output or plot function is called back once per iteration. It can be used by the user to display a message in the console, write into a file, etc... The output or plot function is expected to have the following definition:
myfun <- function(x, optimValues, state)
where the input parameters are:
The current point estimate.
A list which contains the following fields:
The number of function evaluations.
The best function value.
The current iteration number.
The type of step performed. This string depends on the
specific algorithm (see
fminsearch for details).
the state of the algorithm. The following states are available:
when the algorithm is initializing,
when the algorithm is performing iterations,
when the algorithm is terminated.
Return a list with the following fields: Display, FunValCheck, MaxFunEvals, MaxIter, OutputFcn, PlotFcns, TolFun, TolX, nbMatch, boundsAlpha, boxScaling, and alphaMin.
Author of Scilab neldermead module: Michael Baudin (INRIA - Digiteo)
Author of R adaptation: Sebastien Bihorel (email@example.com)
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