Create options structure for optimization functions.
When called without any input or output arguments, optimset
prints
a list of all valid optimization parameters.
When called with one output and no inputs, return an options structure with
all valid option parameters initialized to []
.
When called with a list of parameter/value pairs, return an options structure with only the named parameters initialized.
When the first input is an existing options structure old, the values are updated from either the par/val list or from the options structure new.
Valid parameters are:
Request verbose display of results from optimizations. Values are:
"off"
[default]No display.
"iter"
Display intermediate results for every loop iteration.
"final"
Display the result of the final loop iteration.
"notify"
Display the result of the final loop iteration if the function has failed to converge.
When enabled, display an error if the objective function returns an invalid
value (a complex number, NaN, or Inf). Must be set to "on"
or
"off"
[default]. Note: the functions fzero
and
fminbnd
correctly handle Inf values and only complex values or NaN
will cause an error in this case.
When set to "on"
, the function to be minimized must return a
second argument which is the gradient, or first derivative, of the
function at the point x. If set to "off"
[default], the
gradient is computed via finite differences.
When set to "on"
, the function to be minimized must return a
second argument which is the Jacobian, or first derivative, of the
function at the point x. If set to "off"
[default], the
Jacobian is computed via finite differences.
Maximum number of function evaluations before optimization stops. Must be a positive integer.
Maximum number of algorithm iterations before optimization stops. Must be a positive integer.
A user-defined function executed once per algorithm iteration.
Termination criterion for the function output. If the difference in the
calculated objective function between one algorithm iteration and the next
is less than TolFun
the optimization stops. Must be a positive
scalar.
Termination criterion for the function input. If the difference in x,
the current search point, between one algorithm iteration and the next is
less than TolX
the optimization stops. Must be a positive scalar.
See also: optimget.
Package: octave