158 research outputs found

    On the Distribution of Complex Roots of Random Polynomials with Heavy-tailed Coefficients

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    Consider a random polynomial Gn(z)=ξnzn+...+ξ1z+ξ0G_n(z)=\xi_nz^n+...+\xi_1z+\xi_0 with i.i.d. complex-valued coefficients. Suppose that the distribution of log(1+log(1+ξ0))\log(1+\log(1+|\xi_0|)) has a slowly varying tail. Then the distribution of the complex roots of GnG_n concentrates in probability, as nn\to\infty, to two centered circles and is uniform in the argument as nn\to\infty. The radii of the circles are ξ0/ξτ1/τ|\xi_0/\xi_\tau|^{1/\tau} and ξτ/ξn1/(nτ)|\xi_\tau/\xi_n|^{1/(n-\tau)}, where ξτ\xi_\tau denotes the coefficient with the maximum modulus.Comment: 8 page

    Roots of random polynomials whose coefficients have logarithmic tails

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    It has been shown by Ibragimov and Zaporozhets [In Prokhorov and Contemporary Probability Theory (2013) Springer] that the complex roots of a random polynomial Gn(z)=k=0nξkzkG_n(z)=\sum_{k=0}^n\xi_kz^k with i.i.d. coefficients ξ0,,ξn\xi_0,\ldots,\xi_n concentrate a.s. near the unit circle as nn\to\infty if and only if Elog+ξ0<{\mathbb{E}\log_+}|\xi_0|<\infty. We study the transition from concentration to deconcentration of roots by considering coefficients with tails behaving like L(logt)(logt)αL({\log}|t|)({\log}|t|)^{-\alpha} as tt\to\infty, where α0\alpha\geq0, and LL is a slowly varying function. Under this assumption, the structure of complex and real roots of GnG_n is described in terms of the least concave majorant of the Poisson point process on [0,1]×(0,)[0,1]\times (0,\infty) with intensity αv(α+1)dudv\alpha v^{-(\alpha+1)}\,du\,dv.Comment: Published in at http://dx.doi.org/10.1214/12-AOP764 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Universality for zeros of random analytic functions

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    Let ξ0,ξ1,...\xi_0,\xi_1,... be independent identically distributed (i.i.d.) random variables such that \E \log (1+|\xi_0|)<\infty. We consider random analytic functions of the form Gn(z)=k=0ξkfk,nzk, G_n(z)=\sum_{k=0}^{\infty} \xi_k f_{k,n} z^k, where fk,nf_{k,n} are deterministic complex coefficients. Let νn\nu_n be the random measure assigning the same weight 1/n1/n to each complex zero of GnG_n. Assuming essentially that 1nlogf[tn],nu(t)-\frac 1n \log f_{[tn], n}\to u(t) as nn\to\infty, where u(t)u(t) is some function, we show that the measure νn\nu_n converges weakly to some deterministic measure which is characterized in terms of the Legendre--Fenchel transform of uu. The limiting measure is universal, that is it does not depend on the distribution of the ξk\xi_k's. This result is applied to several ensembles of random analytic functions including the ensembles corresponding to the three two-dimensional geometries of constant curvature. As another application, we prove a random polynomial analogue of the circular law for random matrices.Comment: 26 pages, 8 figures, 1 tabl

    Random determinants, mixed volumes of ellipsoids, and zeros of Gaussian random fields

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    Consider a d×dd\times d matrix MM whose rows are independent centered non-degenerate Gaussian vectors ξ1,...,ξd\xi_1,...,\xi_d with covariance matrices Σ1,...,Σd\Sigma_1,...,\Sigma_d. Denote by Ei\mathcal{E}_i the location-dispersion ellipsoid of ξi:Ei=xRd:xΣi1x1\xi_i:\mathcal{E}_i={\mathbf{x}\in\mathbb{R}^d : \mathbf{x}^\top\Sigma_i^{-1} \mathbf{x}\leqslant1}. We show that EdetM=d!(2π)d/2Vd(E1,...,Ed), \mathbb{E}\,|\det M|=\frac{d!}{(2\pi)^{d/2}}V_d(\mathcal{E}_1,...,\mathcal{E}_d), where Vd(,...,)V_d(\cdot,...,\cdot) denotes the {\it mixed volume}. We also generalize this result to the case of rectangular matrices. As a direct corollary we get an analytic expression for the mixed volume of dd arbitrary ellipsoids in Rd\mathbb{R}^d. As another application, we consider a smooth centered non-degenerate Gaussian random field X=(X1,...,Xk):RdRkX=(X_1,...,X_k)^\top:\mathbb{R}^d\to\mathbb{R}^k. Using Kac-Rice formula, we obtain the geometric interpretation of the intensity of zeros of XX in terms of the mixed volume of location-dispersion ellipsoids of the gradients of Xi/VarXiX_i/\sqrt{\mathbf{Var} X_i}. This relates zero sets of equations to mixed volumes in a way which is reminiscent of the well-known Bernstein theorem about the number of solutions of the typical system of algebraic equations
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