Grassmannian packings: Trust-region stochastic tuning for matrix incoherence

Ming Zhong, Carlos Saltijeral, Josiah Park

Publication date: 13 July 2022

Abstract: We provide a new numerical procedure for constructing low coherence matrices, Trust-Region Stochastic Tuning for Matrix Incoherence (TRSTMI) and detail the results of experiments with a CPU/GPU parallelized implementation of this method. These trials suggest the superiority of this approach over other existing methods when the size of the matrix is large. We also present new conjectures on optimal complex matrices motivated and guided by the experimental results.

Has companion code repository: https://github.com/mindspore-ai/contrib/tree/master/application/TRSTMI

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Grassmannians, Schubert varieties, flag manifolds (14M15) Metric geometry (51F99) General harmonic expansions, frames (42C15)

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