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Create or edit optimization options parameter structure.
Syntax
options = optimset('param1',value1,'param2',value2,...)
optimset
options = optimset
options = optimset(optimfun)
options = optimset(oldopts,'param1',value1,...)
options = optimset(oldopts,newopts)
Description
options = optimset('param1',value1,'param2',value2,...)
creates an optimization options parameter structure called options, in which the specified parameters (param) have specified values. Any unspecified parameters are set to [] (parameters with value [] indicate to use the default value for that parameter when options is passed to the optimization function). It is sufficient to type only enough leading characters to define the parameter name uniquely. Case is ignored for parameter names.
optimset
with no input or output arguments displays a complete list of parameters with their valid values.
options = optimset
(with no input arguments) creates an options structure options where all fields are set to [].
options = optimset(optimfun)
creates an options structure options with all parameter names and default values relevant to the optimization function optimfun.
options = optimset(oldopts,'param1',value1,...)
creates a copy of oldopts, modifying the specified parameters with the specified values.
options = optimset(oldopts,newopts)
combines an existing options structure oldopts with a new options structure newopts. Any parameters in newopts with nonempty values overwrite the corresponding old parameters in oldopts.
Parameters
For more information about individual parameters, see the reference pages for the optimization functions that use these parameters, or Table 1-3. In the lists below, values in { } denote the default value; some parameters have different defaults for different optimization functions and so no values are shown in { }. Optimization parameters used by both large-scale and medium-scale algorithms: Diagnostics [ on | {off} ]
Display [ off | iter | {final} ]
MaxFunEvals [ positive integer ]
Optimization parameters used by large-scale algorithms only:
JacobPattern [ sparse matrix ]
MaxPCGIter [ positive integer ]
PrecondBandWidth [ positive integer | Inf ]
TolPCG [ positive scalar | {0.1} ]
[
DiffMaxChange [ positive scalar | {1e-1} ]
DiffMinChange [ positive scalar | {1e-8} ]
GoalsExactAchieve [ positive scalar integer | {0} ]
HessUpdate [ {bfgs} | dfp | gillmurray | steepdesc ]
LevenbergMarquardt [ on | off ]
LineSearchType [ cubicpoly | {quadcubic} ]
MeritFunction [ singleobj | {multiobj} ]
MinAbsMax [ positive scalar integer | {0} ]
Examples
This statement creates an optimization options structure calledoptions in which the Display parameter is set to 'iter' and the TolFun parameter is set to 1e-8:
options = optimset('Display','iter','TolFun',1e-8)
This statement makes a copy of the options structure called options, changing the value of the TolX parameter and storing new values in optnew:
optnew = optimset(options,'TolX',1e-4);This statement returns an optimization options structure
options that contains all the parameter names and default values relevant to the function fminbnd:
options = optimset('fminbnd')
If you only want to see the default values for fminbnd, you can simply type
optimset fminbndor equivalently
optimset('fminbnd')
See Also
optimget