# what are the scaling factors of Optimization objective and constraint functions ?

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 May 1, 2014, 10:51 what are the scaling factors of Optimization objective and constraint functions ? #1 Senior Member   Join Date: Jun 2011 Posts: 163 Rep Power: 13 Hi all please let me know why the objective and constraint functions are multiplied by the 0.001 ? Best Regards % Optimization objective function with scaling factor % ex= Objective * Scale OPT_OBJECTIVE= DRAG * 0.001 % % Optimization constraint functions with scaling factors, separated by semicolons % ex= (Objective = Value ) * Scale, use '>','<','=' OPT_CONSTRAINT= ( MAX_THICKNESS > 0.04 ) * 0.001 %

 May 15, 2014, 17:16 #2 Member   Sean R. Copeland Join Date: Jan 2013 Posts: 40 Rep Power: 12 Hello mechy, For the optimal design problems, SU2 uses the scipy optimization libraries. There is an initial step that the optimizer takes to perturb the design in its search for the local optimum. We generally scale the objective function to control the size of this initial step to ensure it is not too large. For different objective functions, the value of this scaling factor needs to be adjusted -- in general there's no set of guidelines to select the value, it just takes a little experimentation. -Sean

 January 27, 2015, 09:37 #3 Member   Zeno Join Date: Sep 2013 Location: Delft, The Netherlands Posts: 63 Rep Power: 11 Dear Sean, it happened to me that if I set the scaling factor to a small number ( e.g. OPT_OBJECTIVE=DRAG*0.001) and I start an optimization with already a solution_flow.dat and a solution_adj_cd.dat, the optimization stops at the end of DSN_001 with the following message: done ./DESIGNS/DSN_001 ./DESIGNS/DSN_001 Optimization terminated successfully. (Exit mode 0) Current function value: 0.0003926472324 Iterations: 1 Function evaluations: 1 Gradient evaluations: 1 Where the current function value corresponds exactly to the value of the objective function at the end of the direct simulation multiplied by the scaling factor 0.001. Whereas if the scale is increase to 0.1, or 0.01, the optimization works but it produces negative volume cells at later stages (around DSN_006) and the FGMRES solver diverges. Any hint here? Thanks, Z

 July 3, 2017, 10:47 #4 New Member   Bingfei Join Date: Nov 2015 Posts: 25 Rep Power: 9 I met the same problem these days when optimizing a model made by myself. How did you solve this problem? Does anyone know about this problem? Thank you.

 September 22, 2017, 04:23 #5 New Member   Floris Join Date: Sep 2016 Location: Enschede, The Netherlands Posts: 21 Rep Power: 8 Hi Bingfei, One way to circumvent this problem is to adjust "OPT_ACCURACY". The scaling of the objective function is scaling the termination criteria as well. I hope this helps will solve your problem. Cheers, Floris van der Schuur