An unexpected connection between Bayes A-optimal designs and the group lasso

Please always quote using this URN: urn:nbn:de:0297-zib-73059
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  • We show that the A-optimal design optimization problem over m design points in R^n is equivalent to minimizing a quadratic function plus a group lasso sparsity inducing term over n x m real matrices. This observation allows to describe several new algorithms for A-optimal design based on splitting and block coordinate decomposition. These techniques are well known and proved powerful to treat large scale problems in machine learning and signal processing communities. The proposed algorithms come with rigorous convergence guarantees and convergence rate estimate stemming from the optimization literature. Performances are illustrated on synthetic benchmarks and compared to existing methods for solving the optimal design problem.

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Metadaten
Author:Guillaume Sagnol, Edouard Pauwels
Document Type:Article
Parent Title (English):Statistical Papers
Volume:60
Issue:2
First Page:215
Last Page:234
Year of first publication:2019
Preprint:arXiv:1809.01931
DOI:https://doi.org/https://doi.org/10.1007/s00362-018-01062-y