# Spectrahedral cones generated by rank 1 matrices

arXiv: https://arxiv.org/abs/1409.4781 · HTML: https://mlsanity.com/paper/1409.4781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [On the Exactness of Dantzig-Wolfe Relaxation for Rank Constrained Optimization Problems](https://mlsanity.com/paper/2210.16191/) (2022)

## Abstract (arXiv)

Let cal S_+n subset cal Sn be the cone of positive semi-definite matrices as a subset of the vector space of real symmetric n × n matrices. The intersection of cal S_+n with a linear subspace of cal Sn is called a spectrahedral cone. We consider spectrahedral cones K such that every element of K can be represented as a sum of rank 1 matrices in K. We shall call such spectrahedral cones rank one generated (ROG). We show that ROG cones which are linearly isomorphic as convex cones are also isomorphic as linear sections of the positive semi-definite matrix cone, which is not the case for general spectrahedral cones. We give many examples of ROG cones and show how to construct new ROG cones from given ones by different procedures. We provide classifications of some subclasses of ROG cones, in particular, we classify all ROG cones for matrix sizes not exceeding 4. Further we prove some results on the structure of ROG cones. We also briefly consider the case of complex or quaternionic matrices. ROG cones are in close relation with the exactness of semi-definite relaxations of quadratically constrained quadratic optimization problems or of relaxations approximating the cone of nonnegative functions in squared functional systems.