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| Autor principal: | |
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| Format: | Recurso digital |
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| Publicat: |
Zenodo
2026
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| Accés en línia: | https://doi.org/10.5281/zenodo.19637903 |
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- <p><span>This paper proposes a novel robust matrix constraint modeling method that unifies the linear matrix inequality (LMI) structure with Box uncertainty sets, constructing a unified expression for describing the positive semidefiniteness of matrices under parameter perturbations. Traditional LMI methods primarily handle deterministic or affine parameter systems, while Box uncertainty sets can characterize the worst-case scenario where parameters vary independently within interval boundaries. By introducing "vertex matrix decomposition mapping" and "worst-case boundary projection operator," this paper derives an equivalent verifiable form of the robust linear matrix inequality (RBLMI), thus transforming the infinite constraint problem into a set of finite positive semidefinite matrix constraints. This method has potential applications in robust control, optimization theory, and stability analysis of machine learning models.</span></p>