308. Discretization and reduction in finite and infinite convex optimization
Invited abstract in session TD-3: Variational techniques and subdifferentials, stream Variational analysis: theory and algorithms.
Thursday, 14:10 - 15:50Room: M:J
Authors (first author is the speaker)
| 1. | Abderrahim Hantoute
|
| Mathematics, Universidad de Alicante |
Abstract
We analyze the reduction of the number of constraints in the finite and infinite convex optimization framework, while maintaining the same optimal value. This approach allows a considerable simplification of the study of the duality of such problems under reduced constraints qualifications. We illustrate our result by an explicit characterization of the normal cone to the (effective) domain of a supremum function, necessary to provide more precise optimality conditions.
Keywords
- Semi-infinite optimization
- Convex and non-smooth optimization
Status: accepted
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