Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914275607445504 |
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| author | Lanzillotta, Francesca Albisani, Chiara Pucci, Davide Baracchi, Daniele Piva, Alessandro Lapucci, Matteo |
| author_facet | Lanzillotta, Francesca Albisani, Chiara Pucci, Davide Baracchi, Daniele Piva, Alessandro Lapucci, Matteo |
| contents | In many learning tasks, certain requirements on the processing of individual data samples should arguably be formalized as strict constraints in the underlying optimization problem, rather than by means of arbitrary penalties. We show that, in these scenarios, learning can be carried out exploiting a sequential penalty method that allows to properly deal with constraints. The proposed algorithm is shown to possess convergence guarantees under assumptions that are reasonable in deep learning scenarios. Moreover, the results of experiments on image processing tasks show that the method is indeed viable to be used in practice. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_16812 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing Lanzillotta, Francesca Albisani, Chiara Pucci, Davide Baracchi, Daniele Piva, Alessandro Lapucci, Matteo Machine Learning Image and Video Processing Optimization and Control 68T07, 90C30, 68U10, 90C06, 65K05, 90C26 In many learning tasks, certain requirements on the processing of individual data samples should arguably be formalized as strict constraints in the underlying optimization problem, rather than by means of arbitrary penalties. We show that, in these scenarios, learning can be carried out exploiting a sequential penalty method that allows to properly deal with constraints. The proposed algorithm is shown to possess convergence guarantees under assumptions that are reasonable in deep learning scenarios. Moreover, the results of experiments on image processing tasks show that the method is indeed viable to be used in practice. |
| title | Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing |
| topic | Machine Learning Image and Video Processing Optimization and Control 68T07, 90C30, 68U10, 90C06, 65K05, 90C26 |
| url | https://arxiv.org/abs/2601.16812 |