Name
Title | ||
|---|---|---|
Chance-constrained quasi-convex optimization with application to data-driven switched systems control |
Abstract | ||
|---|---|---|
We study quasi-convex optimization problems, where only a subset of the constraints can be sampled, and yet one would like a probabilistic guarantee on the obtained solution with respect to the initial (unknown) optimization problem. Even though our results are partly applicable to general quasi-convex problems, in this work we introduce and study a particular subclass, which we call "quasi-linear problems". We provide optimality conditions for these problems. Thriving on this, we extend the approach of chance-constrained convex optimization to quasi-linear optimization problems. Finally, we show that this approach is useful for the stability analysis of black-box switched linear systems, from a finite set of sampled trajectories. It allows us to compute probabilistic upper bounds on the JSR of a large class of switched linear systems. |
Year | Venue | DocType |
|---|---|---|
2021 | L4DC | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillaume Berger | 1 | 2 | 5.10 |
| Raphaël M. Jungers | 2 | 222 | 39.39 |
| Zheming Wang | 3 | 30 | 8.12 |