A global optimum-informed greedy algorithm for A-optimal experimental design
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917009642487808 |
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| author | Aarset, Christian |
| author_facet | Aarset, Christian |
| contents | Optimal experimental design (OED) concerns itself with identifying ideal methods of data collection, e.g.~via sensor placement. The \emph{greedy algorithm}, that is, placing one sensor at a time, in an iteratively optimal manner, stands as an extremely robust and easily executed algorithm for this purpose. However, it is a priori unclear whether this algorithm leads to sub-optimal regimes. Taking advantage of the author's recent work on non-smooth convex optimality criteria for OED, we here present a framework for rejection of sub-optimal greedy indices, and study the numerical benefits this offers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A global optimum-informed greedy algorithm for A-optimal experimental design Aarset, Christian Optimization and Control 62K05, 62F15, 35R30, 65K10 Optimal experimental design (OED) concerns itself with identifying ideal methods of data collection, e.g.~via sensor placement. The \emph{greedy algorithm}, that is, placing one sensor at a time, in an iteratively optimal manner, stands as an extremely robust and easily executed algorithm for this purpose. However, it is a priori unclear whether this algorithm leads to sub-optimal regimes. Taking advantage of the author's recent work on non-smooth convex optimality criteria for OED, we here present a framework for rejection of sub-optimal greedy indices, and study the numerical benefits this offers. |
| title | A global optimum-informed greedy algorithm for A-optimal experimental design |
| topic | Optimization and Control 62K05, 62F15, 35R30, 65K10 |
| url | https://arxiv.org/abs/2409.09963 |