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Optimization Using Broyden-Update Self-Adjoint Sensitivities

IEEE AP-S International Symposium Albuquerque NM, June 26, 2006. Optimization Using Broyden-Update Self-Adjoint Sensitivities. Dongying Li, N. K. Nikolova, and M. H. Bakr. (e-mail: lid6@mcmaster.ca ). McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, CANADA.

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Optimization Using Broyden-Update Self-Adjoint Sensitivities

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  1. IEEE AP-S International Symposium Albuquerque NM, June 26, 2006 Optimization Using Broyden-Update Self-Adjoint Sensitivities Dongying Li, N. K. Nikolova, and M. H. Bakr (e-mail:lid6@mcmaster.ca) McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, CANADA Department of Electrical and Computer Engineering Computational Electromagnetics Laboratory

  2. Outline objective & motivation sensitivity analysis • design sensitivity analysis (DSA) • finite difference approximation (FD) • self-adjoint sensitivity analysis (SASA) SASA-based gradient optimization • theory: FD-SASA, B-SASA, B/FD-SASA • numerical results & comparison conclusion and future work

  3. Objective & Motivation applications of DSA gradient based optimization yield and tolerance analysis design of experiments and models p(0) Specs p* Gradient Based Optimizer F(p(i)) p(i) Design Sensitivity Analysis F(p(i)) Numerical EM Solver

  4. Design Sensitivity Analysis Given FEM system equation design variables objective function find subject to

  5. Design Sensitivity Analysis via Finite Differences easy and simple method overhead: at least N additional system analyses

  6. Design Sensitivity Analysis via SASA [N. K. Nikolova, J. Zhu, D. Li, M. Bakr, and J. Bandler, IEEE T-MTT. vol. 54, pp. 670-681, Feb, 2006.] SASA for S-parameters only original system solution needed

  7. Design Sensitivity Analysis via SASA computational overhead

  8. gradient-based algorithms quasi-Newton sequential quadratic programming (SQP) trust-region fast convergence vs. non-gradient based algorithms pattern search neural network-based algorithms genetic algorithms particle swarm guaranteed global minimum SASA-Based Gradient Optimization

  9. SASA-Based Gradient Optimization factors affecting efficiency 1. required number of iterations nature of the algorithm 2. number of simulation calls per iteration nature of the algorithm the Jacobian computation

  10. SASA-Based Gradient Optimization finite-difference SASA (FD-SASA) overhead: N matrix fill Broyden SASA (B-SASA) overhead: practically zero

  11. SASA-Based Gradient Optimization B/FD-SASA guarantees robust derivative computation with minimum time switch between B-SASA and FD-SASA switching criteria from B-SASA to FD-SASA

  12. Example of B/FD-SASA: H-Plane Filter design parameter pT=[L1 L2 L3 W1 W2 W3 W4] initial design p(0)T = [12 14 18 14 11 11 11] (mm) design requirement optimization algorithm TR-minimax [G. Matthaei, L. Young and E. M. T. Jones, Microwave Filters, Impedance–Matching Networks, and Coupling Structures. 1980, pp. 545-547.]

  13. Example of B/FD-SASA: H-Plane Filter Initial design FD optimal B/FD-SASA optimal

  14. Example of B/FD-SASA: H-Plane Filter parameter step size with respect to iterations function value with respect to iterations

  15. finite difference optimal design pT = [12.226 14.042 17.483 14 11 10.922 11.341] (mm) Iterations: 11 time: 3825 s B/FD-SASA optimal design pT = [12.131 13.855 17.80914.01 11.1 11.098 11.191] (mm) Iterations: 7 time: 949 s Example of B/FD-SASA: H-Plane Filter [switching criterion Itriggeredat 5th iteration]

  16. Conclusion summary efficient SASA method for sensitivity analysis implementation of B/FD-SASA on gradient-based optimization: improving efficiency future work further verification of the switching criteria in B/FD- SASA

  17. Thank you

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