Tallennettuna:
| Päätekijä: | |
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| Aineistotyyppi: | Recurso digital |
| Kieli: | |
| Julkaistu: |
Zenodo
2026
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| Linkit: | https://doi.org/10.5281/zenodo.19079668 |
| Tagit: |
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Sisällysluettelo:
- <p><span>This paper proposes a novel generalized optimization framework that organically integrates generalized cone programming (CP) with saddle-point gap analysis, constructing an optimization model that can simultaneously handle constrained cone structures and asymmetric saddle-point errors. This method not only unifies the analytical perspectives of cone programming and dual optimization but also proposes a novel generalized saddle-cononic optimization equation (GSCOE). This model can be applied to various fields such as complex economic system optimization, network flow optimization, and machine learning regularization problems, demonstrating both theoretical originality and practical potential.</span></p>