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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.19637800 |
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Table of Contents:
- <p><span>This paper proposes a novel unified constraint theory that couples and reconstructs linear matrix inequality (LMI) constraints and minimum eigenvalue constraints within the same spectral space. While the traditional LMI form F(x) = F0 + Σ xi Fi ⪰ 0 and the minimum eigenvalue constraint λ_min(A(x)) ≥ 0 are theoretically equivalent to semi-positive definite conditions, they suffer from a separation of expression in terms of optimization structure and numerical stability. This paper introduces a "spectral embedding operator" to construct a new unified constraint form, merging matrix positive definiteness and eigenvalue boundary control into a single operator inequality. This forms a new framework applicable to robust optimization, control system stability analysis, and machine learning constraint modeling.</span></p>