Conference item
Tight generalization guarantees for the sampling and discarding approach to scenario optimization
- Abstract:
- We consider the scenario approach theory to deal with convex optimization programs affected by uncertainty, which is in turn represented by means of scenarios. An approach to deal with such programs while trading feasibility to performance is known as sampling and discarding in the scenario approach literature. Existing bounds on the probability of constraint satisfaction for such programs are not tight. In this paper we use learning theoretic concepts based on the notion of compression to show that for a particular class of convex scenario programs, namely, the so called fully-supported ones, and under a particular scenario discarding scheme, a tight bound can be obtained. We illustrate our developments by means of an example that admits an analytic solution.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Preview, Accepted manuscript, 487.1KB, Terms of use)
-
- Publisher copy:
- 10.1109/CDC42340.2020.9304035
Authors
- Publisher:
- IEEE
- Publication date:
- 2021-01-11
- Acceptance date:
- 2020-07-16
- Event title:
- 59th IEEE Conference on Decision and Control (CDC 2020)
- Event location:
- Jeju Island, South Korea
- Event website:
- https://cdc2020.ieeecss.org/
- Event start date:
- 2020-12-08
- Event end date:
- 2020-12-11
- DOI:
- EISSN:
-
2576-2370
- ISSN:
-
0743-1546
- EISBN:
- 9781728174471
- ISBN:
- 9781728174488
- Language:
-
English
- Keywords:
- Pubs id:
-
1121225
- Local pid:
-
pubs:1121225
- Deposit date:
-
2020-07-27
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2021
- Rights statement:
- © 2021 IEEE.
- Notes:
- This paper was presented at the 59th IEEE Conference on Decision and Control, Jeju Island, South Korea, December 2020. This is the accepted manuscript version of the paper. The final version is available online from IEEE at: https://doi.org/10.1109/CDC42340.2020.9304035
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