English

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 Gr(k,n)\operatorname{Gr}(k,n) is NP-hard. Our results cover all scenarios: (i) when kk and nn are both allowed to grow; (ii) when kk is arbitrary but fixed; (iii) when kk is fixed at its lowest possible value 11. We then deduce the NP-hardness of unconstrained cubic optimization over the Stiefel manifold V(k,n)\operatorname{V}(k,n) and the orthogonal group O(n)\operatorname{O}(n). As an addendum we demonstrate the NP-hardness of unconstrained quadratic optimization over the Cartan manifold, i.e., the positive definite cone S++n\mathbb{S}^n_{\scriptscriptstyle++} regarded as a Riemannian manifold, another popular example in manifold optimization. We will also establish the nonexistence of FPTAS\mathrm{FPTAS} in all cases.

Keywords

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

R2 v1 2026-06-28T17:21:44.734Z