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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16432 |
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| _version_ | 1866909968778657792 |
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| author | Foix-Colonier, Nils Bourguignon, Sébastien |
| author_facet | Foix-Colonier, Nils Bourguignon, Sébastien |
| contents | Linear spectral unmixing under nonnegativity and sum-to-one constraints is a convex optimization problem for which many algorithms were proposed. In practice, especially for supervised unmixing (i.e., with a large dictionary), solutions tend to be sparse due to the nonnegativity of the abundances, thereby motivating the use of an active-set solver. Given the problem specific features, it seems advantageous to design a dedicated algorithm in order to gain computational performance compared to generic solvers. In this paper, we propose to derive such a specific algorithm, while extending the nonnegativity constraints to broader minimum abundance constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An active-set algorithm for spectral unmixing Foix-Colonier, Nils Bourguignon, Sébastien Signal Processing Linear spectral unmixing under nonnegativity and sum-to-one constraints is a convex optimization problem for which many algorithms were proposed. In practice, especially for supervised unmixing (i.e., with a large dictionary), solutions tend to be sparse due to the nonnegativity of the abundances, thereby motivating the use of an active-set solver. Given the problem specific features, it seems advantageous to design a dedicated algorithm in order to gain computational performance compared to generic solvers. In this paper, we propose to derive such a specific algorithm, while extending the nonnegativity constraints to broader minimum abundance constraints. |
| title | An active-set algorithm for spectral unmixing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.16432 |