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This paper considers an inexact primal-dual algorithm for semi-infinite programming (SIP) for which it provides general error bounds. To implement the dual variable update, we create a new prox function for nonnegative measures which turns…

Optimization and Control · Mathematics 2019-01-16 Bo Wei , William B. Haskell , Sixiang Zhao

Various properties of the general two-center two-electron integral over the explicitly correlated exponential function are analyzed for the potential use in high precision calculations for diatomic molecules. A compact one dimensional…

Chemical Physics · Physics 2015-06-11 Krzysztof Pachucki

For the ordinary differential equation (ODE) $\dot{x}(t) = f(t,x)$, $x(0) = x_0$, $t\geq 0$, $x\in R^d$, assume $f$ to be at least continuous in $t$ and locally Lipshitz in $x$, and if necessary, several times continuously differentiable in…

Dynamical Systems · Mathematics 2007-05-23 Divakar Viswanath

Let $p$ be a large prime number and $g$ be any integer of multiplicative order $T$ modulo $p$. We obtain a new estimate of the double exponential sum $$ S=\sum_{n\in \mathcal{N}}\left|\sum_{m\in \mathcal{M} }e_p(an g^{m})\right|, \quad \gcd…

Number Theory · Mathematics 2018-10-16 M. Z. Garaev

A quantum anharmonic oscillator is defined by the Hamiltonian ${\cal H}= -\frac{ {\rm d^{2}}}{{\rm d}x^{2}} + V(x)$, where the potential is given by $V(x) = \sum_{i=1}^{m} c_{i} x^{2i}$ with $c_{m}>0$. Using the Sinc collocation method…

Numerical Analysis · Mathematics 2014-11-19 Philippe Gaudreau , Richard Slevinsky , Hassan Safouhi

Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, optimization, and machine learning. Despite its popularity,…

Optimization and Control · Mathematics 2021-03-24 Thinh T. Doan

We derive a new result for exponential approximation using Stein's method of exchangeable pairs. As an application, an exponential limit theorem with error term is derived for |Tr(U)|^2, where Tr(U) denotes the trace of a matrix chosen from…

Probability · Mathematics 2012-07-24 Jason Fulman , Nathan Ross

Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stability, as well as its competitive performance on $\ell_1$…

Machine Learning · Computer Science 2015-03-17 Ankan Saha , Ambuj Tewari

Purpose of writing this paper is to solve a transcendental function containing a product of a variable and its double exponential by a unique method of approximation. If the value of the said product is given, then its inverse function is…

Numerical Analysis · Mathematics 2025-11-25 Narinder Kumar Wadhawan

Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal…

Machine Learning · Statistics 2022-06-22 Lang Liu , Carlos Cinelli , Zaid Harchaoui

In this work, we determine the full expression for the global truncation error of hyperbolic partial differential equations (PDEs). In particular, we use theoretical analysis and symbolic algebra to find exact expressions for the…

Numerical Analysis · Mathematics 2022-12-05 Siddhartha Bishnu , Mark Petersen , Bryan Quaife

In this paper, we propose a deep learning-based method, deep Euler method (DEM) to solve ordinary differential equations. DEM significantly improves the accuracy of the Euler method by approximating the local truncation error with deep…

Numerical Analysis · Mathematics 2020-03-24 Xing Shen , Xiaoliang Cheng , Kewei Liang

We introduce remarkable upper bounds for the interpolation error constants on triangles, which are sharp and given by simple formulas. These constants are crucial in analyzing interpolation errors, particularly those associated with the…

Numerical Analysis · Mathematics 2025-07-18 Kenta Kobayashi

Solving Linear Ordinary Differential Equations (ODEs) plays an important role in many applications. There are various numerical methods and solvers to obtain approximate solutions. However, few work about global error estimation can be…

Numerical Analysis · Mathematics 2018-04-11 Wenyuan Wu , Wenqiang Yang

F. Stenger proposed efficient approximation formulas for derivatives over infinite intervals. These formulas were derived by combining the Sinc approximation with appropriate conformal maps. It has been demonstrated that these formulas can…

Numerical Analysis · Mathematics 2026-03-03 Tomoaki Okayama , Yuito Kuwashita , Ao Kondo

We consider a degenerate hyperbolic equation of Kirchhoff type with a small parameter epsilon in front of the second-order time-derivative. In a recent paper, under a suitable assumption on initial data, we proved decay-error estimates for…

Analysis of PDEs · Mathematics 2012-03-06 Marina Ghisi , Massimo Gobbino

In this article we establish exponential moment bounds, moment bounds in fractional order smoothness spaces, a uniform H\"older continuity in time, and strong convergence rates for a class of fully discrete exponential Euler-type numerical…

Probability · Mathematics 2021-11-02 Arnulf Jentzen , Felix Lindner , Primož Pušnik

A new numerical method for solving a scalar ordinary differential equation with a given initial condition is introduced. The method is using a numerical integration procedure for an equivalent integral equation and is called in this paper…

Numerical Analysis · Mathematics 2011-09-06 Alexander Lozovskiy

Two exponential wave integrator Fourier pseudospectral (EWI-FP) methods are presented and analyzed for the long-time dynamics of the Dirac equation with small potentials characterized by $\varepsilon \in (0, 1]$ a dimensionless parameter.…

Numerical Analysis · Mathematics 2021-06-29 Yue Feng , Jia Yin

In solving a system of $n$ linear equations in $d$ variables $Ax=b$, the condition number of the $n,d$ matrix $A$ measures how much errors in the data $b$ affect the solution $x$. Estimates of this type are important in many inverse…

Machine Learning · Computer Science 2020-04-29 Tomaso Poggio , Gil Kur , Andrzej Banburski
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