Constrained Nonnegative Gram Feasibility is $\exists\mathbb{R}$-Complete
Angshul Majumdar

TL;DR
This paper proves that determining the existence of a constrained nonnegative Gram factorization is $orall ext{R}$-complete, revealing its high computational complexity even for rank 2, with implications for geometric and algebraic problems.
Contribution
The paper establishes $orall ext{R}$-completeness of constrained nonnegative Gram feasibility for rank 2, using a geometric encoding of arithmetic constraints and extending to all ranks ≥ 2.
Findings
Feasibility problem is $orall ext{R}$-complete for rank 2.
Reduction from ETR-AMI encodes arithmetic constraints geometrically.
Hardness extends to all fixed ranks ≥ 2.
Abstract
We study the computational complexity of constrained nonnegative Gram feasibility. Given a partially specified symmetric matrix together with affine relations among selected entries, the problem asks whether there exists a nonnegative matrix such that satisfies all specified entries and affine constraints. Such factorizations arise naturally in structured low-rank matrix representations and geometric embedding problems. We prove that this feasibility problem is -complete already for rank . The hardness result is obtained via a polynomial-time reduction from the arithmetic feasibility problem \textsc{ETR-AMI}. The reduction exploits a geometric encoding of arithmetic constraints within rank- nonnegative Gram representations: by fixing anchor directions in and representing variables through…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Optimization Algorithms Research · Polynomial and algebraic computation · Complexity and Algorithms in Graphs
