One discrete Gaussian sample at an arbitrary parameter can be drawn in 2^(n/2+o(n)) expected time, resolving an open question from ADRS15.
On the extreme zeros of Jacobi polynomials
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
By applying the Euler--Rayleigh methods to a specific representation of the Jacobi polynomials as hypergeometric functions, we obtain new bounds for their largest zeros. In particular, we derive upper and lower bound for $1-x_{nn}^2(\lambda)$, with $x_{nn}(\lambda)$ being the largest zero of the $n$-th ultraspherical polynomial $P_n^{(\lambda)}$. For every fixed $\lambda>-1/2$, the limit of the ratio of our upper and lower bounds for $1-x_{nn}^2(\lambda)$ does not exceed $1.6$. This paper is a continuation of [1].
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One Discrete Gaussian Sample in $2^{n/2+o(n)}$ Time
One discrete Gaussian sample at an arbitrary parameter can be drawn in 2^(n/2+o(n)) expected time, resolving an open question from ADRS15.