Constrained optimization is based on a generalized reduced gradient algorithm. There are several parameters that control the behavior of this algorithm. Each parameter has a default value that is appropriate for most problems. You are not required to take any action to use the default parameter settings; however, at times, it may be necessary to set one or more of the parameters to a new value to make the optimizer more efficient or make it possible to solve a difficult problem.
The Advanced options are in the System > Optimization Properties dialog box. When you click on Advanced, a dialog box showing all the optimization parameters is displayed. You can change any value displayed. The new values remain in effect for the duration of the session.
The following table describes the advanced parameters.
|
Parameter |
Description |
Default Value | ||
|
doscale |
Scaling. 0 No scaling. 1 The problem is scaled so that the maximum value of any row or column of the initial gradient array is less than or equal to 1.0. |
doscale = 0 | ||
|
epinit |
To run the problem with epnewt initially set fairly large and then tighten at the end of the optimization, assign epinit the initial tolerance and epnewt the final one. |
epinit = Error Tolerance (Optimization Properties) | ||
|
epnewt |
A constraint is assumed to be binding if it is within epnewt of one of its bounds. epnewt and epinit should be set together so that epinit ≤ epnewt. |
epnewt = Error Tolerance (Optimization Properties) | ||
|
epskt |
The convergence criteria and requires that the K-T factor is ≤ epskt. |
epskt = 0.01 | ||
|
epspiv |
If, in constructing the basis inverse, the absolute value of a prospective pivot element is less than epspiv, the pivot is rejected and another pivot element is being sought. |
epspiv = Error Tolerance (Optimization Properties) | ||
|
epstop |
This specifies the convergence criteria. If the fractional change in the objective function is less than epstop for nstop consecutive iterations, and if the K-T factor is ≤ epskt, the program accepts the current point as optimal. Embed accepts the current point as optimal if the Kuhn-Tucker optimality conditions are satisfied to within epstop, that is, if the K-T factor is ≤ epstop. |
epstop = Error Tolerance*10 (Optimization Properties) | ||
|
ipr |
Print level for Embed report. 0 Print initial and final variable and function values 1 Print initial and final variable and function values plus one summary line for each one dimensional search. Values of ipr > 1 and ≤ 6 are permitted, but require knowledge of the internal workings of Embed and are not recommended for general use. |
ipr = 1 | ||
|
iquad |
Method for initial estimates of basic variables for each one-dimensional search. 0 Tangent vectors and linear extrapolation 1 Quadratic extrapolation |
iquad = 0 | ||
|
itlim |
If the Newton procedure takes itlim iterations without converging, the iterations are stopped and corrective action taken. |
itlim = 10 | ||
|
limeval |
Limit on the number of simulation runs. limeval=0 permits an unlimited number of simulation runs. |
limeval = Max Iterations (Optimization Properties) | ||
|
limser |
If the number of completed 1D searches exceeds limser, Embed terminates and returns inform = 3. |
limser = 10000 | ||
|
maximize |
The objective function is maximized if maximize = 1. The default is to minimize the objective function. |
maximize = 0 | ||
|
monitor |
The report produced by Embed is written to VSMGRG2.TXT located in the directory with the current diagram. Setting monitor = 1 instructs Embed to display the report while the optimization run is being performed. The monitor option provides a convenient way to keep track of long optimization runs. The monitor displays the Embed report in a window with menu items that can be used to save the report in a file for future reference. |
monitor = 0 | ||
|
nstop |
If the fractional change in the objective function is less than epstop for nstop consecutive iterations, Embed accepts the current point as optimal. |
nstop = 3 | ||
|
ph1eps |
If ph1eps is nonzero, the phase 1 objective is augmented by a multiple of the true objective. The multiple is selected so that, at the initial point, the ratio of the true objective and the sum of the infeasibilities is ph1eps. Setting ph1eps = 0.0 produces the most efficient way to reach a feasible point (a point where all constraints are satisfied). Setting ph1eps > 0.0 causes Embed to reach feasibility without ignoring the objective function. |
ph1eps = 0.0 | ||
|
pstep |
This is the step size used for estimating partial derivatives of functions with respect to the variables. |
pstep = Error Tolerance (Optimization Properties) | ||