Iterated integrals of Jacobi polynomials

Let P(α,β)n be the n-th monic Jacobi polynomial with α,β>−1. Given m numbers ω1,…,ωm∈C∖[−1,1], let Ωm=(ω1,…,ωm) and P(α,β)n,m,Ωm be the m-th iterated integral of (n+m)!n!P(α,β)n normalized by the conditions dkP(α,β)n,m,Ωmdzk(ωm−k)=0, for k=0,1,…,m−1. The main purpose of the paper is to study the algebraic and asymptotic properties of the sequence of monic polynomials {P(α,β)n,m,Ωm}n. In particular, we obtain the relative asymptotic for the ratio of the sequences {P(α,β)n,m,Ωm}n and {P(α,β)n}n. We prove that the zeros of these polynomials accumulate on a suitable ellipse.The research of H. Pijeira was supported by research Grant MTM2015-65888-C4-2-P Ministerio de Economía y Competitividad of Spain

Expansion of Iterated Ito Stochastic Integrals of Arbitrary Multiplicity Based on Generalized Multiple Fourier Series Converging in the Mean

The article is devoted to the expansions of iterated Ito stochastic integrals based on generalized multiple Fourier series converging in the sence of norm in the space $L_2([t, T]^k),$ $k\in\mathbb{N}.$ The method of generalized multiple Fourier series for expansion and mean-square approximation of iterated Ito stochastic integrals of arbitrary multiplicity $k$ ($k\in\mathbb{N}$) with respect to components of the multidimensional Wiener process is proposed and developed. The obtained expansions contain only one operation of the limit transition in contrast to existing analogues. In the article it is also obtained the generalization of the proposed method for discontinuous complete orthonormalized systems of functions in the space $L_2([t, T]^k),$ $k\in\mathbb{N}$ as well as for the complete orthonormalized systems of functions with weight $r(t_1)\ldots r(t_k)$ in the space $L_2([t, T]^k),$ $k\in\mathbb{N}$. The comparison of the considered method with the well-known expansions of iterated Ito stochastic integrals based on Ito formula and Hermite polynomials is given. The convergence in the mean of degree $2n$ $(n \in \mathbb{N})$ and with probability 1 of the proposed method is proved.Comment: 70 pages. Sect. 11 was adde

A Distributed Procedure for Computing Stochastic Expansions with Mathematica

The solution of a (stochastic) differential equation can be locally approximated by a (stochastic) expansion. If the vector field of the differential equation is a polynomial, the corresponding expansion is a linear combination of iterated integrals of the drivers and can be calculated using Picard Iterations. However, such expansions grow exponentially fast in their number of terms, due to their specific algebra, rendering their practical use limited. We present a Mathematica procedure that addresses this issue by re-parametrising the polynomials and distributing the load in as small as possible parts that can be processed and manipulated independently, thus alleviating large memory requirements and being perfectly suited for parallelized computation. We also present an iterative implementation of the shuffle product (as opposed to a recursive one, more usually implemented) as well as a fast way for calculating the expectation of iterated Stratonovich integrals for Brownian Motion.Comment: 15 pages, 2 figures. Submitte

A distributed procedure for computing stochastic expansions with Mathematica

The solution of a (stochastic) differential equation can be locally approximated by a (stochastic) expansion. If the vector field of the differential equation is a polynomial, the corresponding expansion is a linear combination of iterated integrals of the drivers and can be calculated using Picard Iterations. However, such expansions grow exponentially fast in their number of terms, due to their specific algebra, rendering their practical use limited. We present a Mathematica procedure that addresses this issue by reparametrizing the polynomials and distributing the load in as small as possible parts that can be processed and manipulated independently, thus alleviating large memory requirements and being perfectly suited for parallelized computation. We also present an iterative implementation of the shuffle product (as opposed to a recursive one, more usually implemented) as well as a fast way for calculating the expectation of iterated Stratonovich integrals for Brownian motion

Comparative Analysis of the Efficiency of Application of Legendre Polynomials and Trigonometric Functions to the Numerical Integration of Ito Stochastic Differential Equations

The article is devoted to comparative analysis of the efficiency of application of Legendre polynomials and trigonometric functions to the numerical integration of Ito stochastic differential equations in the framework of the method of approximation of iterated Ito and Stratonovich stochastic integrals based on generalized multiple Fourier series. On the example of iterated Ito stochastic integrals of multiplicities 1 to 3, included in the Taylor-Ito expansion, it is shown that expansions of stochastic integrals based on Legendre polynomials are much easier and require significantly less computational costs compared to their analogues obtained using the trigonometric system of functions. The results of the article can be useful for construction of strong numerical methods for Ito stochastic differential equations.Comment: 25 pages. Some corrections in Sect. 5, 6. Minor changes along the text in the whole. arXiv admin note: substantial text overlap with arXiv:1801.08862, arXiv:1801.00231, arXiv:1807.02190, arXiv:1802.00643, arXiv:1806.1070

Decomposition of elliptic multiple zeta values and iterated Eisenstein integrals

We describe a decomposition algorithm for elliptic multiple zeta values, which amounts to the construction of an injective map $\psi$ from the algebra of elliptic multiple zeta values to a space of iterated Eisenstein integrals. We give many examples of this decomposition, and conclude with a short discussion about the image of $\psi$. It turns out that the failure of surjectivity of $\psi$ is in some sense governed by period polynomials of modular forms.Comment: v2, minor change