A basic hypothesis of this paper is that the multiplier methods can be effective and efficient for dynamic response optimization of large scale systems. The methods have been previously shown to be inefficient compared to the primal methods for static response applications. However, they can be more efficient for dynamic response applications because they collapse all time-dependent constraints and the cost function to one functional. This can result in substantial savings in the computational effort during design sensitivity analysis. To investigate this hypothesis, an augmented functional for the dynamic response optimization problem is defined. Design sensitivity analysis for the functional is developed and three example problems are solved to investigate computational aspects of the multiplier methods. It is concluded that multiplier methods can be effective for dynamic response problems but need numerical refinements to avoid convergence difficulties in unconstrained minimization.

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