Representations of quasi-Newton matrices and their use in limited memory methods
Mathematical Programming: Series A and B
Optimization: algorithms and consistent approximations
Optimization: algorithms and consistent approximations
Statistics and Computing
Engineering computation under uncertainty - Capabilities of non-traditional models
Computers and Structures
Designing robust structures - A nonlinear simulation based approach
Computers and Structures
Efficient strategies for reliability-based optimization involving non-linear, dynamical structures
Computers and Structures
Reliability sensitivity estimation of linear systems under stochastic excitation
Computers and Structures
Sequential Monte Carlo for rare event estimation
Statistics and Computing
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In this paper the feasibility of using a particular feasible direction interior point algorithm for solving reliability-based optimization problems of high dimensional stochastic dynamical systems is investigated. The optimal design problem is formulated in terms of an inequality constrained non-linear optimization problem. A class of interior point algorithms based on the solution of the first-order optimality conditions is considered here. For this purpose, a quasi-Newton iteration is used to solve the corresponding nonlinear system of equations. Several numerical examples are presented to illustrate the feasibility of the proposed methodology.