A common engineering practice is the use of approximation models in place of expensive computer simulations to drive a multidisciplinary design process based on nonlinear programming techniques. The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem. To date the primary focus of most approximate optimization strategies is that application of the method should lead to improved designs. This is a laudable attribute and certainly relevant for practicing designers. However to date few researchers have focused on the development of approximate optimization strategies that are assured of converging to a solution of the original problem. Recent works based on trust region model management strategies have shown promise in managing convergence in unconstrained approximate minimization. In this research we extend these well established notions from the literature on trust-region methods to manage the convergence of the more general approximate optimization problem where equality, inequality and variable bound constraints are present. The primary concern addressed in this study is how to manage the interaction between the optimization and the fidelity of the approximation models to ensure that the process converges to a solution of the original constrained design problem. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, one can show that the optimization process converges to a solution of the original problem. In this research an approximate optimization strategy is developed in which a cumulative response surface approximation of the augmented Lagrangian is sequentially optimized subject to a trust region constraint. Results for several test problems are presented in which convergence to a Karush-Kuhn-Tucker (KKT) point is observed.
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e-mail: jrenaud@derosa.ame.nd.edu
e-mail: itw@vt.edu
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March 1998
Research Papers
Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization
J. F. Rodri´guez,
J. F. Rodri´guez
Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556
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J. E. Renaud,
J. E. Renaud
Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556
e-mail: jrenaud@derosa.ame.nd.edu
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L. T. Watson
L. T. Watson
Departments of Computer Science and Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0106
e-mail: itw@vt.edu
Search for other works by this author on:
J. F. Rodri´guez
Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556
J. E. Renaud
Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556
e-mail: jrenaud@derosa.ame.nd.edu
L. T. Watson
Departments of Computer Science and Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0106
e-mail: itw@vt.edu
J. Mech. Des. Mar 1998, 120(1): 58-66 (9 pages)
Published Online: March 1, 1998
Article history
Received:
January 1, 1997
Online:
December 11, 2007
Citation
Rodri´guez, J. F., Renaud, J. E., and Watson, L. T. (March 1, 1998). "Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization." ASME. J. Mech. Des. March 1998; 120(1): 58–66. https://doi.org/10.1115/1.2826677
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