Abstract
We study the relaxation complexity for nonconvex quadratic global optimization, which is defined as the number of convex relaxation subproblems to be solved. The relaxation complexity for quadratic programming with fixed nonconvex-rank is known to be a polynomial function of the dimension. In this paper, we show that the relaxation complexity for nonconvex quadratic optimization with convex quadratic constraints may not depend on the dimension, as long as the objective function has a fixed nonconvex rank.
| Original language | English |
|---|---|
| Article number | 19 |
| Journal | Communications in Optimization Theory |
| Volume | 2024 |
| DOIs | |
| State | Published - 2024 |
Keywords
- Dikin ellipsoid
- Global optimization
- Löwner-John ellipsoid
- Quadratic constrained quadratic optimization
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