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In his 2006 paper, Jin proves that Kalantaris bounds on polynomial zeros, indexed by $m leq 2$ and called $L_m$ and $U_m$ respectively, become sharp as $mrightarrowinfty$. That is, given a degree $n$ polynomial $p(z)$ not vanishing at the origin and an error tolerance $epsilon > 0$, Jin proves that there exists an $m$ such that $frac{L_m}{rho_{min}} > 1-epsilon$, where $rho_{min} := min_{rho:p(rho) = 0} left|rhoright|$. In this paper we derive a formula that yields such an $m$, thereby constructively proving Jins theorem. In fact, we prove the stronger theorem that this convergence is uniform in a sense, its rate depending only on $n$ and a few other parameters. We also give experimental results that suggest an optimal m of (asymptotically) $Oleft(frac{1}{epsilon^d}right)$ for some $d ll 2$. A proof of these results would show that Jins method runs in $Oleft(frac{n}{epsilon^d}right)$ time, making it efficient for isolating polynomial zeros of high degree.
The Modified Szpiro Conjecture, equivalent to the $abc$ Conjecture, states that for each $epsilon>0$, there are finitely many rational elliptic curves satisfying $N_{E}^{6+epsilon}<max!left{ leftvert c_{4}^{3}rightvert,c_{6}^{2}right} $ where $c_{4}$
A 1993 result of Alon and Furedi gives a sharp upper bound on the number of zeros of a multivariate polynomial over an integral domain in a finite grid, in terms of the degree of the polynomial. This result was recently generalized to polynomials ove
Let $A$ be an algebra and let $f(x_1,...,x_d)$ be a multilinear polynomial in noncommuting indeterminates $x_i$. We consider the problem of describing linear maps $phi:Ato A$ that preserve zeros of $f$. Under certain technical restrictions we solve t
In this article, we consider the preconditioned Hamiltonian Monte Carlo (pHMC) algorithm defined directly on an infinite-dimensional Hilbert space. In this context, and under a condition reminiscent of strong log-concavity of the target measure, we p
In this paper, we develop a symmetric accelerated stochastic Alternating Direction Method of Multipliers (SAS-ADMM) for solving separable convex optimization problems with linear constraints. The objective function is the sum of a possibly nonsmooth