Rational Approximations to Certain Algebraic Numbers

W.M.Schmit[11] conjectured that for any$\;\theta$ with deg$\;\theta\geq 3,$ there is no constant$\;C=C(\theta)$ so that$\;|p-q\theta|>Cq^{-1}$ for every rationa$\;p/q.$ [12,p26] states that the computations of the first several thousand partial quotients for such numbers as$\;\sqrt[3]{2}$ and$\;\sqrt[3]{3}$ support the conjecture that the sequence of partial quotients is unbounded. In this paper, applying Dirichlet's approximation theorem to certain algebraic numbers$\;\theta,$ e.g.$\;\theta=\sqrt[n]{d},d\in N,n\geq 3,d>0;$ $\;\theta^{3}+b_{1}\theta-b_{0}=0,b_{0}>0;$ $\;\theta^{4}+b_{2}\theta^{2}-b_{0}=0,\;b_{0}>0.$ We proved that there exists a effective constant$\;C=C(\theta)$ such that$\;|p-q\theta|>Cq^{-1}$ for all$\;p/q.$ Our theorem shows their sequence of partial quotients can not be unbounded

The Bane of Low-Dimensionality Clustering

In this paper, we give a conditional lower bound of $n^{\Omega(k)}$ on running time for the classic k-median and k-means clustering objectives (where n is the size of the input), even in low-dimensional Euclidean space of dimension four, assuming the Exponential Time Hypothesis (ETH). We also consider k-median (and k-means) with penalties where each point need not be assigned to a center, in which case it must pay a penalty, and extend our lower bound to at least three-dimensional Euclidean space. This stands in stark contrast to many other geometric problems such as the traveling salesman problem, or computing an independent set of unit spheres. While these problems benefit from the so-called (limited) blessing of dimensionality, as they can be solved in time $n^{O(k^{1-1/d})}$ or $2^{n^{1-1/d}}$ in d dimensions, our work shows that widely-used clustering objectives have a lower bound of $n^{\Omega(k)}$, even in dimension four. We complete the picture by considering the two-dimensional case: we show that there is no algorithm that solves the penalized version in time less than $n^{o(\sqrt{k})}$, and provide a matching upper bound of $n^{O(\sqrt{k})}$. The main tool we use to establish these lower bounds is the placement of points on the moment curve, which takes its inspiration from constructions of point sets yielding Delaunay complexes of high complexity

Recurrence and algorithmic information

In this paper we initiate a somewhat detailed investigation of the relationships between quantitative recurrence indicators and algorithmic complexity of orbits in weakly chaotic dynamical systems. We mainly focus on examples.Comment: 26 pages, no figure