[param_opt,opt_info] = sdo.optimize(opt_fcn,param)
[param_opt,opt_info] = sdo.optimize(opt_fcn,param,options)
[param_opt,opt_info] = sdo.optimize(prob)
[
uses param_opt
,opt_info
] = sdo.optimize(opt_fcn
,param
)fmincon
(the default optimization method)
to solve a design optimization problem of the form:
$$\underset{p}{\text{min}}F(p)\text{subjectto}\{\begin{array}{l}{C}_{leq}(p)\le 0\hfill \\ {C}_{eq}(p)=0\hfill \\ A\times p\le B\hfill \\ {A}_{eq}\times p=Beq\hfill \\ lb\le p\le ub\hfill \end{array}$$
where
p — Design variable
C_{leq}, C_{eq} — Nonlinear inequality and equality constraints
A, B — Linear inequality constraints
A_{eq}, B_{eq} — Linear equality constraints
lb, ub — Upper and lower bounds on p
[
specifies
the optimization options. For parameter estimation, you typically
use the Nonlinear Least Squares method:param_opt
,opt_info
] = sdo.optimize(opt_fcn
,param
,options
)
opts = sdo.OptimizeOptions('Method','lsqnonlin');
[
uses
a structure that contains the function to be minimized, design variables
and optimization options.param_opt
,opt_info
] = sdo.optimize(prob
)

Function to be minimized. The optimization solver calls this function during optimization. The function requires:
For an example, type 

A 

Optimization options.


Structure with the following fields:


A 

Optimization information. Structure with one or more of the following fields:

function_handle (@)
 param.Continuous
 sdo.OptimizeOptions