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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19568381 |
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Table of Contents:
- <p class="17"><span>This paper proposes a novel optimization theoretical framework that unifies the classical S-Lemma and Robust Linear Programming (RLP) into a single mathematical structure. Traditional RLP deals with the worst-case scenario of linear constraints under uncertain sets, while the S-Lemma provides the necessary and sufficient conditions for transforming quadratic inequality implications into linear matrix inequalities (LMIs). This paper constructs a "quadratic structure parameterized uncertainty matrix perturbation model," which allows robust linear constraints to be uniformly transformed into single solvable convex optimization conditions through the S-Lemma, resulting in a new unified equation system. This model theoretically extends the solvable boundary of robust optimization and structurally establishes a bridge between "linear constraints—quadratic uncertainty—LMI solvability."</span></p>