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The approximate degree of a Boolean function $f(x_{1},x_{2},\ldots,x_{n})$ is the minimum degree of a real polynomial that approximates $f$ pointwise within $1/3$. Upper bounds on approximate degree have a variety of applications in…

Computational Complexity · Computer Science 2018-01-16 Alexander A. Sherstov

Conventional finite-difference schemes for solving partial differential equations are based on approximating derivatives by finite-differences. In this work, an alternative theory is proposed which view finite-difference schemes as…

Numerical Analysis · Mathematics 2013-09-23 Siu A. Chin

We investigate superconvergence properties of the spectral interpolation involving fractional derivatives. Our interest in this superconvergence problem is, in fact, twofold: when interpolating function values, we identify the points at…

Numerical Analysis · Mathematics 2015-03-25 Xuan Zhao , Zhimin Zhang

In this paper, we present a new numerical method to solve fractional differential equations. Given a fractional derivative of arbitrary real order, we present an approximation formula for the fractional operator that involves integer-order…

Numerical Analysis · Mathematics 2015-12-16 Ricardo Almeida , Nuno R. O. Bastos

This paper gives the existence and uniqueness results for solution of fractional differential equations with Hilfer derivative. Using some new techniques and generalizing the restrictive conditions imposed on considered function, the…

Classical Analysis and ODEs · Mathematics 2017-09-29 D. B. Dhaigude , Sandeep P. Bhairat

This paper considers binomial approximation of continuous time stochastic processes. It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by binomial…

Computational Finance · Quantitative Finance 2015-02-09 Nikolai Dokuchaev

This paper presents two methods for approximating a proper subset of the entries of a Hessian using only function evaluations. These approximations are obtained using the techniques called \emph{generalized simplex Hessian} and…

Numerical Analysis · Mathematics 2025-05-14 Gabriel Jarry-Bolduc , Chayne Planiden

This paper presents a novel multi-scale method for elliptic partial differential equations with arbitrarily rough coefficients. In the spirit of numerical homogenization, the method constructs problem-adapted ansatz spaces with uniform…

Numerical Analysis · Mathematics 2024-08-05 Philip Freese , Moritz Hauck , Tim Keil , Daniel Peterseim

Spherical radial basis functions are used to define approximate solutions to strongly elliptic pseudodifferential equations on the unit sphere. These equations arise from geodesy. The approximate solutions are found by the Galerkin and…

Numerical Analysis · Mathematics 2013-11-27 T. D. Pham , T. Tran

This paper deals with the algorithmic aspects of solving feasibility problems of semidefinite programming (SDP), aka linear matrix inequalities (LMI). Since in some SDP instances all feasible solutions have irrational entries, numerical…

Optimization and Control · Mathematics 2025-04-28 Vladimir Kolmogorov , Simone Naldi , Jeferson Zapata

A deep approximation is an approximating function defined by composing more than one layer of simple functions. We study deep approximations of functions of one variable using layers consisting of low-degree polynomials or simple conformal…

Numerical Analysis · Mathematics 2025-04-25 Kingsley Yeon

Complex polynomial optimization has recently gained more and more attention in both theory and practice. In this paper, we study the optimization of a real-valued general conjugate complex form over various popular constraint sets including…

Optimization and Control · Mathematics 2016-12-08 Taoran Fu , Bo Jiang , Zhening Li

A fundamental problem in numerical analysis and approximation theory is approximating smooth functions by polynomials. A much harder version under recent consideration is to enforce bounds constraints on the approximating polynomial. In…

Numerical Analysis · Mathematics 2021-12-28 Larry Allen , Robert C. Kirby

We consider finite element approximations of ill-posed elliptic problems with conditional stability. The notion of {\emph{optimal error estimates}} is defined including both convergence with respect to mesh parameter and perturbations in…

Numerical Analysis · Mathematics 2024-03-25 Erik Burman , Mihai Nechita , Lauri Oksanen

This paper builds on the notion of the so-called orthogonal derivative, where an n-th order derivative is approximated by an integral involving an orthogonal polynomial of degree n. This notion was reviewed in great detail in a paper in J.…

Classical Analysis and ODEs · Mathematics 2014-07-08 E. Diekema

Recently, the authors Khalil, R., Al Horani, M., Yousef. A. and Sababheh, M., in " A new Denition Of Fractional Derivative, J. Comput. Appl. Math. 264. pp. 6570, 2014. " introduced a new simple well-behaved definition of the fractional…

Dynamical Systems · Mathematics 2016-11-25 Thabet Abdeljawad

This article offers sharp spatial and temporal mean-square regularity results for a class of semi-linear parabolic stochastic partial differential equations (SPDEs) driven by infinite dimensional fractional Brownian motion with the Hurst…

Numerical Analysis · Mathematics 2020-08-04 Xiaojie Wang , Ruisheng Qi , Fengze Jiang

We investigate random compact sets with random functions defined thereon, such as polynomials, rational functions, the pluricomplex Green function and the Siciak extremal function. One surprising consequence of our study is that randomness…

Complex Variables · Mathematics 2020-11-06 Paul M. Gauthier , Thomas Ransford , Simon St-Amant , Jérémie Turcotte

Considering fractional fast diffusion equations on bounded open polyhedral domains in $\mathbb{R}^N$, we give a fully Galerkin approximation of the solutions by $C^0$-piecewise linear finite elements in space and backward Euler…

Numerical Analysis · Mathematics 2019-12-18 Dongxue Li , Youquan Zheng

Given a univariate polynomial, its abscissa is the maximum real part of its roots. The abscissa arises naturally when controlling linear differential equations. As a function of the polynomial coefficients, the abscissa is H{\"o}lder…

Optimization and Control · Mathematics 2015-07-31 Roxana Heß , Didier Henrion , Jean-Bernard Lasserre , Tien Son Pham
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