Abstract
In this paper, we consider a subset selection problem in a spatial field where we seek to find a set of k locations whose observations provide the best estimate of the field value at a finite set of prediction locations. The measurements can be taken at any location in the continuous field, and the covariance between the field values at different points is given by the widely used squared exponential covariance function. One approach for observation selection is to perform a grid discretization of the space and obtain an approximate solution using the greedy algorithm. The solution quality improves with a finer grid resolution but at the cost of increased computation. We propose a method to reduce the computational complexity, or conversely to increase solution quality, of the greedy algorithm by considering a search space consisting only of prediction locations and centroids of cliques formed by the prediction locations. We demonstrate the effectiveness of our proposed approach in simulation, both in terms of solution quality and runtime.
Original language | English |
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Title of host publication | Proceedings European Control Conference (ECC) 2022 |
Publisher | IEEE |
Pages | 1067-1072 |
ISBN (Electronic) | 978-3-907144-07-7 |
ISBN (Print) | 978-1-6654-9733-6 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 European Control Conference (ECC) - London, United Kingdom Duration: 12 Jul 2022 → 15 Jul 2022 |
Publication series
Name | 2022 European Control Conference, ECC 2022 |
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Conference
Conference | 2022 European Control Conference (ECC) |
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Country/Territory | United Kingdom |
City | London |
Period | 12/07/22 → 15/07/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Greedy algorithms
- runtime
- costs
- Computational modeling
- Europe
- Estimation
- Computational complexity