202 research outputs found
Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization
This paper considers structural optimization under a reliability constraint,
where the input distribution is only partially known. Specifically, when we
only know that the expected value vector and the variance-covariance matrix of
the input distribution belong to a given convex set, we require that, for any
realization of the input distribution, the failure probability of a structure
should be no greater than a specified target value. We show that this
distributionally-robust reliability constraint can be reduced equivalently to
deterministic constraints. By using this reduction, we can treat a
reliability-based design optimization problem under the distributionally-robust
reliability constraint within the framework of deterministic optimization,
specifically, nonlinear semidefinite programming. Two numerical examples are
solved to show relation between the optimal value and either the target
reliability or the uncertainty magnitude
A mixed 0-1 programing approach to topology-finding of tensegrity structures
p. 569-576In this paper we propose an optimization-based approach to finding a tensegrity structure
based on the ground structure method. We first solve a problem which maximizes the number of struts over the self-equilibrium condition and the discontinuity condition of struts.
Subsequently we solve the minimization problem of the number of cables in order to remove redundant self-equilibrium modes. The optimization problem at each step can be formulated as a mixed integer programming (MIP) problem. The method does not require any connectivity information of cables and struts to be known in advance, while the obtained tensegrity structure is guaranteed to satisfy the discontinuity condition of struts rigorously.Ehara, S.; Kanno, Y. (2009). A mixed 0-1 programing approach to topology-finding of tensegrity structures. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/654
A feasible smoothing accelerated projected gradient method for nonsmooth convex optimization
Smoothing accelerated gradient methods achieve faster convergence rates than
that of the subgradient method for some nonsmooth convex optimization problems.
However, Nesterov's extrapolation may require gradients at infeasible points,
and thus they cannot be applied to some structural optimization problems. We
introduce a variant of smoothing accelerated projected gradient methods where
every variable is feasible. The convergence rate is obtained
using the Lyapunov function. We conduct a numerical experiment on the robust
compliance optimization of a truss structure.Comment: 6 pages, 2 figure
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