REVIEW 1 cited by
The condition number of a randomly perturbed matrix
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Let $M$ be an arbitrary $n$ by $n$ matrix. We study the condition number a random perturbation $M+N_n$ of $M$, where $N_n$ is a random matrix. It is shown that, under very general conditions on $M$ and $M_n$, the condition number of $M+N_n$ is polynomial in $n$ with very high probability. The main novelty here is that we allow $N_n$ to have discrete distribution.
Forward citations
Cited by 1 Pith paper
-
Assessing Quantum Advantage for Gaussian Process Regression
Quantum algorithms for Gaussian process regression lose their exponential speedup because kernel matrix condition numbers grow at least linearly with dataset size.
Discussion (0). Continue with ORCID to comment.