Search arXiv⌕ Search

arXiv · 2105.00018

Lyapunov exponent for products of random Ising transfer matrices: the balanced disorder case

Abstract

We analyze the top Lyapunov exponent of the product of sequences of two by two matrices that appears in the analysis of several statistical mechanics models with disorder: for example these matrices are the transfer matrices for the nearest neighbor Ising chain with random external field, and the free energy density of this Ising chain is the Lyapunov exponent we consider. We obtain the sharp behavior of this exponent in the large interaction limit when the external field is centered: this balanced case turns out to be critical in many respects. From a mathematical standpoint we precisely identify the behavior of the top Lyapunov exponent of a product of two dimensional random matrices close to a diagonal random matrix for which top and bottom Lyapunov exponents coincide. In particular, the Lyapunov exponent is only $\log$-Hölder continuous.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giambattista Giacomin, Rafael L. Greenblatt. 2022-02-24. Lyapunov exponent for products of random Ising transfer matrices: the balanced disorder case. https://doi.org/10.30757/alea.v19-27

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Selection in the Zero-Noise Limit for High-Dimensional Diffusions with Measurable Drift

This paper investigates the zero-noise limit of high-dimensional small-noise diffusion processes governed by the stochastic differential equation (SDE) \begin{equation*} \mathrm{d}X_{t}^{\varepsilon }=b(X_{t}^{\varepsilon })\,\mathrm{d}t+\varepsilon \,\mathrm{d}W_{t},\quad X_{0}^{\varepsilon }=0,\quad \varepsilon >0, \end{equation*} where the drift coefficient $b$ is assumed to be measurable and bounded. Under this condition, the associated deterministic ordinary differential equation (ODE) $\dot{x}_{t}=b(x_{t})$ may admit multiple Filippov solutions, i.e., solutions in the sense of differential inclusions, due to the lack of Lipschitz continuity or uniqueness criteria. However, the introduction of non-degenerate additive noise restores well-posedness: the perturbed system admits a unique strong (pathwise) solution for each $\varepsilon >0$. Our analysis is based on the radial-spherical decomposition. We prove that, under certain assumptions, the radius dominates a one-dimensional comparison process and escapes to infinity at a linear rate, and the extrinsic angular martingale converges. Under an explicit separable gradient structure, expressed in terms of a radial potential $V(r,ω)=\langle b(rω),ω\rangle $, the angle converges, for every fixed noise, almost surely to the set of global angular maximizers of $V$, and its distance from that set tends to zero in probability as $\varepsilon\rightarrow0$. The limiting measure is compact, carried by immediate-departure Filippov paths, and, under a Lipschitz radial-graph condition, purely singular with Hausdorff dimension at most $d-1$. A continuous-mapping criterion for exit-time convergence is also established.

math.PR↗

Asymptotic optimality of dynamic first-fit packing on the half-axis

We revisit a classical problem in dynamic storage allocation. Items arrive in a linear storage medium, modeled as a half-axis, at a Poisson rate $r$ and depart after an independent exponentially distributed unit mean service time. The arriving item sizes (lengths) are assumed to be independent and identically distributed (i.i.d.) from a common distribution $H$. A widely employed algorithm for allocating the items is the "first-fit" discipline, namely, each arriving item is placed in the left-most vacant interval large enough to accommodate it. In a seminal 1985 paper, Coffman, Kadota, and Shepp ([6]) proved that in the special case of unit length items (i.e. degenerate $H$), as $r$ tends towards infinity, the first-fit algorithm is asymptotically optimal in the following sense: the steady-state ratio of expected "empty space" (gaps between items) to expected occupied space tends towards $0$. In a sequel to [6], Coffman, Kadota, and Shepp ([5]) conjectured that the first-fit discipline is also asymptotically optimal for non-degenerate $H$. In this paper we provide the first proof of first-fit asymptotic optimality for non-degenerate distributions $H$ of item sizes. Our main result is for the case when $H$ is concentrated on countably many positive real sizes forming an increasing sequence that is either finite or goes to infinity, with the average item size being finite. We prove that under the first-fit discipline, as $r$ tends towards infinity, the steady-state packing configuration (scaled down by $r$) converges in distribution to the limiting packing configuration with smaller items on the left, larger items on the right, and with no gaps between. In particular, this proves asymptotic optimality of first-fit in the following sense: if $P$ is the expected occupied space, then in steady-state the empty space (scaled down by $r$) in $[0,P]$ vanishes.

math.PR↗

Mind the jumps: well-posedness of semi-martingale 2BSDEs

We develop a well-posedness theory for second-order backward stochastic differential equations (2BSDEs) with jumps. This work covers two complementary notions of solution: an extrinsic and an intrinsic one. We also discuss the obstruction to aggregating jump integrands. The chosen framework allows for controlled diffusions with jumps, pure-jump processes, and discrete-time processes in a unified setting.

math.PR↗