Design optimization problem solution
[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)
F — Cost (objective)
p — Design variable
Cleq, Ceq — Nonlinear inequality and equality constraints
A, B — Linear inequality constraints
Aeq, Beq — Linear equality constraints
lb, ub — Upper and lower bounds on p
opts = sdo.OptimizeOptions('Method','lsqnonlin');
Cost function to be minimized. The optimization solver calls this function during optimization.
The function requires:
For an example, type
Structure with the following fields:
Optimization information. Structure with one or more of the following fields:
Create design variables.
p = param.Continuous('x',1);
Specify optimization options.
opts = sdo.OptimizeOptions; opts.GradFcn = 'on';
Optimize the parameter.
[pOpt,opt_info] = sdo.optimize(@(p) sdoExampleCostFunction(p),p,opts);
Optimization started 29-Feb-2020 03:54:13 max First-order Iter F-count f(x) constraint Step-size optimality 0 3 1 0 1 5 0.09 0 0.7 0.59 2 6 0.0716349 0.001047 0.0324 0.0129 3 7 0.0717968 9.127e-08 0.000302 2.37e-06 Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
By default, the software displays the optimization information for each iteration in the MATLAB® command window. To learn more about the information displayed, see:
You can configure the level of this display using the
of an optimization option set.