39. Uncertain standard quadratic optimization under distributional assumptions: a chance-constrained epigraphic approach
Invited abstract in session WD-2: Conic and polynomial optimization, stream Conic optimization: theory, algorithms and applications.
Wednesday, 11:25 - 12:40Room: M:O
Authors (first author is the speaker)
| 1. | Immanuel Bomze
|
| Dept. of Statistics and OR, University of Vienna | |
| 2. | Daniel de Vicente
|
| University of Vienna |
Abstract
The standard quadratic optimization problem (StQP) consists of minimizing a quadratic form over the standard simplex. Without convexity or concavity of the quadratic form, the StQP is NP-hard. This problem has many interesting applications. Sometimes, the data matrix is uncertain. We investigate models where the distribution of the data matrix is known but where both the StQP after realization of the data matrix and the here-and-now problem are indefinite.
Keywords
- Optimization under uncertainty and applications
- SS - Conic Optimization and Applications
- Linear and nonlinear optimization
Status: accepted
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