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- W2027349279 abstract "We consider the problem of construction of optimal experimental designs (approximate theory) on a compact subset X of Rd with nonempty interior, for a concave and Lipschitz differentiable design criterion ϕ(·) based on the information matrix. The proposed algorithm combines (a) convex optimization for the determination of optimal weights on a support set, (b) sequential updating of this support using local optimization, and (c) finding new support candidates using properties of the directional derivative of ϕ(·). The algorithm makes use of the compactness of X and relies on a finite grid Xℓ⊂X for checking optimality. By exploiting the Lipschitz continuity of the directional derivatives of ϕ(·), efficiency bounds on X are obtained and ϵ-optimality on X is guaranteed. The effectiveness of the method is illustrated on a series of examples." @default.
- W2027349279 created "2016-06-24" @default.
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- W2027349279 date "2014-11-01" @default.
- W2027349279 modified "2023-09-25" @default.
- W2027349279 title "Algorithmic construction of optimal designs on compact sets for concave and differentiable criteria" @default.
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- W2027349279 doi "https://doi.org/10.1016/j.jspi.2014.04.005" @default.
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