Local optimality conditions and local saddle point theorems for nonconvex robust programming
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Bibliographic record
Abstract
This paper concerns the study of a class of nonconvex programming problems with data uncertainty in both the objective and constraints.We first introduce two new constraint qualifications in terms of the tangential subdifferential of the involved functions.Under the new constraint qualifications, we provide some necessary and sufficient conditions for KKT-type local optimality conditions to hold.Similarly, local saddle point theorems and local total Lagrange dualities for robust nonconvex programming problems are also given.
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| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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