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We present two approaches for computing rational approximations to multivariate functions, motivated by their effectiveness as surrogate models for high-energy physics (HEP) applications. Our first approach builds on the Stieltjes process…

Numerical Analysis · Mathematics 2021-03-12 Anthony P. Austin , Mohan Krishnamoorthy , Sven Leyffer , Stephen Mrenna , Juliane Muller , Holger Schulz

We perform certain alternating binomial summations with parameters that occur in the analysis of algorithms. A combination of integral and special function and special number representations is used. The results are sufficiently general to…

Mathematical Physics · Physics 2007-05-23 Mark W. Coffey

By considering an empirical approximation, and a new class of operators that we will call walking operators, we construct, for any positive ND-toeplitz matrix, an infinite in all dimensions matrix, for which the inverse approximates the…

Spectral Theory · Mathematics 2007-05-23 Rami Kanhouche

We investigate convergence of the expectation maximization algorithm by representing it as a generalized proximal method. Convergence of iterates and not just in value is investigated under natural hypotheses such as definability of the…

Statistics Theory · Mathematics 2026-01-06 Dominikus Noll

For a class of non-selfadjoint $h$--pseudodifferential operators with double characteristics, we give a precise description of the spectrum and establish accurate semiclassical resolvent estimates in a neighborhood of the origin.…

Analysis of PDEs · Mathematics 2011-05-25 Michael Hitrik , Karel Pravda-Starov

It is well known that there are entire functions whose orbit approximates any other entire function under the action of a sequence of translation operators . This result also holds for an uncountable family of sequences of translation…

Functional Analysis · Mathematics 2022-03-02 Nikolaos Tsirivas

We present a 'calculator' for constructing a homogeneous approximation of nonlinear control systems, which is based on the algebraic approach developed by the authors in their previous papers. This approach mainly uses linear algebraic and…

Optimization and Control · Mathematics 2021-12-01 Grigory Sklyar , Pavel Barkhayev , Svetlana Ignatovich , Viktor Rusakov

Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes regularization. Most analyses of iteration…

Optimization and Control · Mathematics 2019-01-25 Ching-pei Lee , Stephen J. Wright

We obtain approximation formulas for fractional integrals and derivatives of Riemann-Liouville and Marchaud types with a variable fractional order. The approximations involve integer-order derivatives only. An estimation for the error is…

Classical Analysis and ODEs · Mathematics 2013-10-29 Ricardo Almeida , Delfim F. M. Torres

We present Korovkin approximation theorems that incorporate summability methods. These result allows us to obtain a unified treatment of several previous results, focusing on the underlying structure and the properties that a summability…

Functional Analysis · Mathematics 2023-07-07 M. del Carmen Listán-García , María Pilar Romero de la Rosa

We prove new summability properties for multilinear operators on $\ell_p$ spaces. An important tool for this task is a better understanding of the interplay between almost summing and absolutely summing multilinear operators.

Functional Analysis · Mathematics 2014-04-07 O. Blasco , G. Botelho , D. Pellegrino , P. Rueda

The inverse of an $\infty \times \infty$ symmetric band matrix can be constructed in terms of a matrix continued fraction. For Hamiltonians with Coulomb plus polynomial potentials, this results in an exact and analytic Green's operator…

Nuclear Theory · Physics 2013-10-30 N. C. Brown , S. E. Grefe , Z. Papp

In the present paper, we consider $(p,q)$-analogue of the Baskakov-Beta operators and using it, we estimate some direct results on approximation. Also, we represent the convergence of these operators graphically using MATLAB.

Classical Analysis and ODEs · Mathematics 2016-08-29 Neha Malik , Vijay Gupta

Some finite series of harmonic numbers involving certain reciprocals are evaluated. Products of such reciprocals are expanded in a sum of the individual reciprocals, leading to a computer program. A list of examples is provided.

Number Theory · Mathematics 2012-03-08 Maarten Kronenburg

By applying the derivative operator to the corresponding hypergeometric form of a $q$-series transformation due to Andrews [1,Theorem 4], we establish a general harmonic number identity. As the special cases of it, several interesting…

Combinatorics · Mathematics 2011-11-15 Chuanan Wei , Dianxuan Gong

In this paper we give a definition of "algorithm," "finite algorithm," "equivalent algorithms," and what it means for a single algorithm to dominate a set of algorithms. We define a derived algorithm which may have a smaller mean execution…

Data Structures and Algorithms · Computer Science 2007-05-23 Kerry M. Soileau

We give necessary and sufficient conditions for the convergence with geometric rate of the common denominators of simultaneous rational interpolants with a bounded number of poles. The conditions are expressed in terms of intrinsic…

Complex Variables · Mathematics 2015-02-15 J. Cacoq , B. de la Calle Ysern , G. López Lagomasino

This paper is a survey of extensions to finite automata theory to model real-time systems as well as systems exhibiting mixed discrete-continuous behavior. Real-time systems maintain a continuous and timely interaction with the environment,…

Formal Languages and Automata Theory · Computer Science 2018-11-27 Lakhan Shiva Kamireddy

In this paper we combine two existing approaches for approximating attractors. One of them approximates the attractors arbitrarily well by sublevel sets related to solutions of infinite dimensional linear programming problems. A downside…

Optimization and Control · Mathematics 2023-10-06 Corbinian Schlosser

In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closed-form…

Machine Learning · Statistics 2015-06-02 Nicholas G. Polson , James G. Scott , Brandon T. Willard