Algorithmic construction of optimal designs on compact sets for concave and differentiable criteria

Pronzato, Luc and Zhigljavsky, Anatoly A. ORCID: https://orcid.org/0000-0003-0630-8279 2014. Algorithmic construction of optimal designs on compact sets for concave and differentiable criteria. Journal of Statistical Planning and Inference 154 , pp. 141-155. 10.1016/j.jspi.2014.04.005

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Abstract

We consider the problem of construction of optimal experimental designs (approximate theory) on a compact subset XX of RdRd with nonempty interior, for a concave and Lipschitz differentiable design criterion ϕ(·)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(·)ϕ(·). The algorithm makes use of the compactness of XX and relies on a finite grid Xℓ⊂XXℓ⊂X for checking optimality. By exploiting the Lipschitz continuity of the directional derivatives of ϕ(·)ϕ(·), efficiency bounds on XX are obtained and ϵ -optimality on XX is guaranteed. The effectiveness of the method is illustrated on a series of examples.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Elsevier
ISSN:	0378-3758
Date of Acceptance:	30 April 2014
Last Modified:	01 Nov 2022 09:55
URI:	https://orca.cardiff.ac.uk/id/eprint/89665

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