The MISQP method can be used with the MOP solver or a direct solver call. It allows you to generate a new sample set to provide a more refined approach than the Screening method. MISQP is available for both continuous and discrete input parameters, which is why mixed is in its name. MISQP can handle only one output parameter goal. Other output parameters can be defined as constraints.
Further information about methods of multidisciplinary optimization used in optiSLang can be found here.
Node Settings
To access the options shown in the following table, double-click the Mixed-Integer Sequential Quadratic Programming (MISQP) system on the Scenery pane and in the Settings tab, click .
Option | Default Value | Description |
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Maximum Number of Iterations | 50 |
Stop criterion. Maximum number of iterations that the algorithm is to execute. If convergence happens before this number is reached, the iterations stop. This also provides an idea of the maximum possible number of samples that are needed for the full cycle. For MISQP, the number of samples can be approximated according to the Finite Difference Approximation gradient calculation method as follows: number of iterations * (2*number of inputs +1) |
Maximum Convergence Percentage | 0.0001 |
Stop criterion. Tolerance to which the Karush-Kuhn-Tucker (KKT) optimality criterion is generated during the MISQP process. A smaller value indicates more convergence iterations and a more accurate (but slower) solution. A larger value indicates fewer convergence iterations and a less accurate (but faster) solution. |
Initial Finite Difference Delta | 0.001 |
Defines the relative variation used to alter the current point to compute gradients. Used in conjunction with Maximum convergence percentage (%) to ensure that the delta in MISQP's calculation of finite differences is large enough to be seen above the noise in the simulation problem. This wider sampling produces results that are more clearly differentiated so that the difference is less affected by solution noise and the gradient direction is clearer. The value should be larger than both the value for Initial Finite Difference Delta (%) and the noise magnitude of the model. However, smaller values produce more accurate results, so set Initial Finite Difference Delta (%) only as high as necessary to be seen above simulation noise. |
Derivative Approximation | Central |
Defines the method of approximating the gradient of the objective function. Choices are:
|
Additional Options
To access the options shown in the following table, in any tab, click
.Option | Description |
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Limit maximum in parallel |
Controls the resource usage of nodes in the system. When the check box is cleared (default), a value is chosen to ensure the best possible utilization of the child nodes. When the check box is selected, set the value manually to specify how many designs are sent to child nodes, limiting the maximum degree of parallelism for all children. Ansys recommends keeping the check box clear. |
Auto-save behavior |
Select one of the following options:
The project, including the database, is auto-saved (depending on defined interval) after calculating this node/system (either when the calculation succeeds or fails). By default, all parametric and algorithm systems have selected, all other nodes have selected. |
Supported versions
The following versions of MISQP are supported and tested: 2021 R2 and later.