For the option: | Do the following: |
|---|---|
Category | Set to Optimization. |
Algorithm | Specify the algorithm used to perform the optimization. The MSCADS algorithms are provided with Adams View using the MSC Automated Design Synthesis code. The DOT algorithms can be purchased from Vanderplaats R&D, Inc. You can also include your own optimization algorithm. The contact information for Vanderplaats R&D, Inc. is: Vanderplaats R&D, Inc. 1767 S. 8th Street, Suite. 100 Colorado Springs, CO 80906 More about Algorithms. |
Tolerance | Specify the limit below which subsequent differences of the objective must fall before an optimization is considered successful. If the condition: ABS(objective[now] - objective[now-1]) < convergence_tolerance is true for a certain number of iterations (usually two), then the convergence tolerance criterion is met. Note that this is only one test that is made by most optimization algorithms before they terminate successfully. Like other Adams Solver tolerances, you may need to experiment with this tolerance to find the right value for your application. Display the objective versus iteration Strip chart. (See Solver Settings - Display) If the optimizer quits even though the last iteration made noticeable progress, try reducing the tolerance. If the optimizer continues iterating even after the objective has stopped changing very much, make the tolerance larger. |
Max. Iterations | Set how many iterations the optimization algorithm should take before it admits failure. Note that a single iteration can have an arbitrarily large number of analysis runs. |
Rescale | Enter the number of iterations after which the design variable values are rescaled. If you set the value to -1, scaling is turned off. |
Differencing | Control how the optimizer computes gradients for the design functions. Centered differencing perturbs each design variable in the negative direction from the nominal value, then again in the positive direction using finite differencing between the perturbed results to compute the gradient. If you choose forward differencing, each design variable is perturbed in a positive direction only. Centered differencing can sometimes generate smoother, more reliable gradients (especially in noisy models), but it causes twice as many analysis runs to be performed. |
More | Click to set more advanced options, listed below. |
Increment | The differencing increment specifies the size of increment to use when performing finite differencing to compute gradients. When using forward differencing, this value is added to the nominal value of each design variable on successive runs. When using central differencing, this value is first subtracted from the nominal value and then added to it. Smaller increments may give more accurate approximations of the gradient, but are also more susceptible to random variations from run to run. Larger increments help minimize the effects of variations, but give less accurate gradients. |
Debug | Set to display messages from the optimizer. Turning on debugging output sends copious optimizer diagnostics to the window that launched Adams View. Keep an eye on that window anyway, as some important warnings might be written there. The debugging output shows you the data the optimizer is receiving from Adams View, among other things. If the optimizer is behaving erratically, this may help you determine the source of the problem. |
User | Adams View passes the user parameters to a user-written optimization algorithm. Realizing that there may be parameter information that is not conveyed through the existing parameter set, this parameter was added to allow you to pass any real numeric data to your algorithm. |
Min. Converged | The number of consecutive iterations for which the absolute or relative convergence criteria must be met to indicate convergence in the DOT Sequential Linear Programming method. |