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Engineering optimization dilemma Optimization algorithms developed by mathematicians are normally based on linear and quadratic approximations Usually.

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Presentation on theme: "Engineering optimization dilemma Optimization algorithms developed by mathematicians are normally based on linear and quadratic approximations Usually."— Presentation transcript:

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2 Engineering optimization dilemma Optimization algorithms developed by mathematicians are normally based on linear and quadratic approximations Usually have proofs of convergence to local optimum (Karush-Kuhn-Tucker points) Engineers often use approximations motivated by problem-specific knowledge They conduct sequential approximate optimization –Define a box; approximate in the box; optimize based on approximation; move the center of the box to the approximate optimum No easy way to determine box size, no proofs

3 Approximation management framework (AMF) John Dennis at Rice University developed methodology for general approximations for unconstrained problems His students carried work further for constrained problems We use paper by two of them (Natalia Alexandrov of NASA Langley and Michael Lewis of the College of William and Mary)

4 Trust region For approximations, trust region refers to where the approximation is sufficiently accurate. Some approximations (e.g. Taylor series) can be made very accurate if the region is small enough. For optimization, a key measure of the accuracy is the ratio between actual and predicted improvement in the objective. Good improvement ratio means getting the slope approximately right. Example: If range of values in box is only 5%, any approximation is likely to have small error, but not necessary improvement ratio close to 1.

5 Example We minimize the function f=1-sinx using the (Taylor series) approximation f a =1-x, starting at x=0. If our box is |x|<0.5 the solution is x=0.5, f=0.52, f a =0.5. Expected improvement, 0.5, actual improvement Improvement ratio is Possibly box is too small. If our box is |x|<1 the solution is x=1, f=0.16, f a =0. Expected improvement, 1, actual improvement Looks reasonable If our box is |x|<2 the solution is x=2, f=0.09, f a =-1. Expected improvement, 2, actual improvement Improvement ratio is Possibly box is too large If our box is |x|<4 the solution is x=4, f=1.8, f a =-3. Expected improvement, 4, actual improvement Box is too large!

6 Trust region size management algorithm Optimization in box of function f using approximation f a Improvement ratio at approximate optimum x* If r>0 accept new point, otherwise just change box size

7 Box-size algorithm

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9 Requirement for convergence For proof of convergence, you need that you can make the error as small as needed by reducing the size of the box. To satisfy this condition, they modify the approximation near the center of the box using Haftka, R.T., “Combining Global and Local Approximations,” AIAA Journal, Vol. 29, No. 9, pp , 1991 The approach creates a hybrid between original approximation and Taylor series approximation near the center, but requires derivatives there.

10 Augmented Lagrangian version

11 3D Wing optimization Analysis: Euler (CFL3D) Conditions: Objective: -L/D Constraints: lower bound on lift, upper bounds on pitching moment and rolling moment coefficients Low-fidelity analysis 95x25x17 mesh 8 min. CPU High fidelity analysis: 193x49x33 mesh, 64min.

12 Comparison of models.

13 Savings Algorithm Improvement. Ratios of savings in function evaluations/derivative calculations (Each low fidelity calculation counts as 1/8 evaluation) Augmented Lagrangian: 3.0/2.6 (kriging) SQP 3.0/3.0 (polynomial) MAESTRO 1.9/1.9 (CFD)


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