Author | Alp Dener, Jason E. Hicken |
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Title | Revisiting Individual Discipline Feasible using matrix-free Inexact-Newton-Krylov |
Year | 2014 |
Journal | 10th AIAA Multidisciplinary Design Optimization Conference |
School | Rensselaer Polytechnic Institute |
Abstract | The individual-discipline-feasible (IDF) formulation was proposed to simplify the imple- mentation of MDO problems. The IDF formulation introduces coupling variables into the optimization problem that eliminate the need for a full multidisciplinary analysis at each optimization iteration; this simplifies the solution of MDO problems by maintaining mod- ularity of the discipline software. Historically, the MDO community has used conventional optimization algorithms to solve IDF-formulated problems. Conventional optimizers are not well suited to IDF, because they use limited-memory quasi-Newton methods (linear convergence) and require the constraint Jacobian explicitly. The cost of computing the coupling-variable constraint Jacobian is prohibitively expensive for high-fidelity IDF prob- lems. Matrix-free Reduced-Space inexact-Newton-Krylov (RSNK) algorithms overcome these issues, because they scale superlinearly and do not require the constraint Jacobian explicitly. Therefore, this class of algorithm has great potential to solve IDF-formulated MDO problems in a scalable and efficient manner. In this paper, we describe the applica- tion of RSNK to the IDF formulation and compare its performance to the multidisciplinary feasible architecture. |
PDF File | Download |
DOI Link | 10.2514/6.2014-0110 |