Consider the quotient of a real Hilbert space by a subgroup of its ortho...
In 1992, Godsil and Hensel published a ground-breaking study of
distance...
Given a real inner product space V and a group G of linear isometries, m...
Given a finite-dimensional real inner product space V and a finite subgr...
We introduce a sketch-and-solve approach to speed up the Peng-Wei
semide...
Given a real inner product space V and a group G of linear isometries, w...
An equi-isoclinic tight fusion frame (EITFF) is a type of Grassmannian c...
Over-complete systems of vectors, or in short, frames, play the role of
...
Given an arbitrary matrix A∈ℝ^n× n, we consider the
fundamental problem ...
We study tight projective 2-designs in three different settings. In the
...
Neural collapse is an emergent phenomenon in deep learning that was rece...
We formulate explicit predictions concerning the symmetry of optimal cod...
A standard model for epidemics is the SIR model on a graph. We introduce...
We introduce an extension to local principal component analysis for lear...
Many clustering problems enjoy solutions by semidefinite programming.
Th...
We extend the techniques of Hügel, Rauhut and Strohmer (Found. Comput.
M...
For d∈{5,6}, we classify arrangements of d + 2 points in
RP^d-1 for whic...
We introduce a new Procrustes-type method called matching component anal...
The Lovász theta number is a semidefinite programming bound on the cliqu...
We study lines through the origin of finite-dimensional complex vector s...
We apply the method of moments to prove a recent conjecture of Haikin, Z...
How can we arrange n lines through the origin in three-dimensional
Eucli...
The line packing problem is concerned with the optimal packing of points...
Recent progress in Zauner's conjecture has leveraged deep conjectures in...
Compressed sensing is the art of reconstructing structured n-dimensional...
Inspired by the word game Ghost, we propose a new protocol for bipartisa...
Given labeled points in a high-dimensional vector space, we seek a
low-d...
Gerrymandering is a long-standing issue within the U.S. political system...
We study lines through the origin of finite-dimensional complex vector s...
It has been experimentally established that deep neural networks can be ...
Efficient algorithms for k-means clustering frequently converge to
subop...
We introduce a model-free relax-and-round algorithm for k-means clusteri...
Recently, Awasthi et al. introduced an SDP relaxation of the k-means
pro...
Group model selection is the problem of determining a small subset of gr...