10. Learning to Relax Nonconvex Quadratically Constrained Quadratic Programs
Invited abstract in session WC-2: Conic and Semidefinite Optimization, stream Conic optimization: theory, algorithms and applications.
Wednesday, 10:05 - 11:20Room: M:O
Authors (first author is the speaker)
| 1. | Burak Kocuk
|
| Industrial Engineering, Sabanci University | |
| 2. | Buket Özen
|
| Industrial Engineering, Sabancı University |
Abstract
Nonconvex quadratically constrained quadratic programs are NP-Hard to solve in general. Literature primarily employs either semidefinite or linear programming to relax them, which are usually effective for distinct problem types, and a holistic understanding of which relaxation should be preferred over the other for a given instance is lacking. In this research, we present a learning-based approach to predict whether a semidefinite or linear programming relaxation would produce a stronger bound for a given instance by examining spectral properties and sparsity patterns of the data matrices.
Keywords
- Conic and semidefinite optimization
- Linear and nonlinear optimization
Status: accepted
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