Configuring the Sequential Quadratic Programming (NLPQL) Technique

From the Flow section of the action bar, click Optimization and drop it on the process diagram.
Double-click Optimization .
The Optimization Editor appears.
From the General tab's Optimization Technique list, select NLPQL.

In the Optimization Technique Options area, enter or select the following:

Option	Description
Maximum Iterations	The maximum number of design iterations you want the optimizer to run. The value type is integer. The default value is 40. Other possible values are $\geq 1$ .
Termination Accuracy	The termination criterion for NLPQL. The NLPQL stopping algorithm uses several alternative convergence checks, with the main convergence parameter based on the Karush-Kuhn-Tucker necessary optimality condition and the complementary slackness. Termination accuracy is applied in such a way that the scale of the objective and constraint parameters has little or no effect on the convergence check. The accuracy of the gradients calculation must be considered when selecting the Termination Accuracy value. If the subflow outputs are accurate up to 8–10 digits and the calculated gradients have at least 7 accurate digits, the recommended Termination Accuracy value is $1 \times 10^{7}$ . If the gradient’s accuracy is lower, the Termination Accuracy value must be increased to $1.0 \times 10^{- 5} ...1.0 \times 10^{- 4}$ . The value type is real. The default value is $1 \times 10^{- 6}$ . Other possible values are $0 \leq T e r m i n a t i o n A c c u r a c y \leq 1.0$ .
Relative Step Size	The relative finite difference step size for the creation of the linear process. The value type is real. The default value is 0.0010 (0.1%). Other possible values are $> 0.0$ .
Minimum Absolute Step Size	The minimum absolute finite difference step for the creation of the linear process. The value type is real. The default value is $1 \times 10^{- 4}$ . Other possible values are $> 0.0$ .
Gradient Points	NLPQL supports higher-order gradient approximation formulas, increasing the number of points which are evaluated in parallel and the accuracy of the approximation. Gradient Points is an integer specifying the number of additional points (per design variable) that will be evaluated during the gradient calculation. A value of 1 corresponds to the standard forward-difference formulation $[f_{(x + h)} - f_{(x)}] / h$ ; a value of 2 corresponds to the slightly more accurate central-difference formulation $[f_{(x + h)} - f_{(x - h)}] / 2 h$ . The default value is $1$ . Possible values are $1 \leq Gradient Points \leq 5$ .
Save Technique Log	Most optimization techniques create a log file of information/messages as they run. This information can be useful for determining why an optimizer took the path that it did or why it converged. Some of these log files can get extremely large, so they are not stored with the run results by default. Select this option if you want to store the log with the run results (as a document) for later viewing.
Maximum Failed Runs	The maximum number of failed subflow evaluations that can be tolerated by the optimization technique. If the number of failed runs exceeds this value, the optimization adapter will terminate execution. To disable this feature, set this option to any negative value (for example, –1). When this option is set to a negative value, the optimization will continue execution despite any number of failed subflow runs.
Failed Run Penalty Value	The value of the Penalty parameter that is used for all failed subflow runs. The default value is $1 \times 10^{30}$ .
Failed Run Objective Value	The value of the Objective parameter that is used for all failed subflow runs. The default value is $1 \times 10^{30}$ .

Click Ok to save your changes and to close the Optimization Editor.