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548. Uncertain standard quadratic optimization under distributional assumptions: a chance-constrained epigraphic approach
Invited abstract in session TA-35: Optimization under uncertainty: theory and solution algorithms, stream Stochastic, Robust and Distributionally Robust Optimization.
Tuesday, 8:30-10:00Room: 44 (building: 303A)
Authors (first author is the speaker)
| 1. | Daniel de Vicente
|
| University of Vienna | |
| 2. | Immanuel Bomze
|
| Dept. of Statistics and OR, University of Vienna |
Abstract
The standard quadratic optimization problem (StQP) consists of minimizing a quadratic form over the standard simplex. If the quadratic form is neither convex nor concave, the StQP is NP-hard. This problem has many relevant real-world applications ranging from portfolio optimization to machine learning.
To accommodate uncertainty in the data matrix, we present a model with known distribution of the data matrix where both the StQP after realization, and the here-and-now problem are indefinite. Similarly to some robust formulations of uncertain StQPs, it turns out that a chance-constrained epigraphic StQP is in fact equivalent to a deterministic (possibly non-convex) StQP. We test the performance of a chance-constrained epigraphic StQP to the uncertain StQP by simulation.
Keywords
- Programming, Stochastic
- Programming, Quadratic
Status: accepted
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