Grassmannian optimization is NP-hard
Optimization and Control
2024-12-10 v2 Numerical Analysis
Numerical Analysis
Abstract
We show that unconstrained quadratic optimization over a Grassmannian is NP-hard. Our results cover all scenarios: (i) when and are both allowed to grow; (ii) when is arbitrary but fixed; (iii) when is fixed at its lowest possible value . We then deduce the NP-hardness of unconstrained cubic optimization over the Stiefel manifold and the orthogonal group . As an addendum we demonstrate the NP-hardness of unconstrained quadratic optimization over the Cartan manifold, i.e., the positive definite cone regarded as a Riemannian manifold, another popular example in manifold optimization. We will also establish the nonexistence of in all cases.
Cite
@article{arxiv.2406.19377,
title = {Grassmannian optimization is NP-hard},
author = {Zehua Lai and Lek-Heng Lim and Ke Ye},
journal= {arXiv preprint arXiv:2406.19377},
year = {2024}
}
Comments
21 pages