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Using methods developed in multivariate splines, we present an explicit formula for discrete truncated powers, which are defined as the number of non-negative integer solutions of linear Diophantine equations. We further use the formula to…

Combinatorics · Mathematics 2010-06-17 Zhiqiang Xu

We propose novel methods for approximate sampling recovery and integration of functions in the Freud-weighted Sobolev space $W^r_{p,w}(\mathbb{R})$. The approximation error of sampling recovery is measured in the norm of the Freud-weighted…

Numerical Analysis · Mathematics 2026-01-06 Dinh Dũng

We establish a deterministic and stochastic spherical quasi-interpolation framework featuring scaled zonal kernels derived from radial basis functions on the ambient Euclidean space. The method incorporates both quasi-Monte Carlo and Monte…

Numerical Analysis · Mathematics 2025-10-15 Zhengjie Sun , Mengyuan Lv , Xingping Sun

This work is devoted to a vast extension of Sanov's theorem, in Laplace principle form, based on alternatives to the classical convex dual pair of relative entropy and cumulant generating functional. The abstract results give rise to a…

Probability · Mathematics 2019-12-12 Daniel Lacker

The paper is devoted to some applications of Stepanov method. In the first part of the paper we obtain the estimate of the cardinality of the set, which is obtained as an intersection of additive shifts of some different subgroups of F^*_p.…

Number Theory · Mathematics 2015-05-07 Ilya D. Shkredov , Elena V. Solodkova , Ilya V. Vyugin

We develop the convergence theory for a well-known method for the interpolation of functions on the real axis with rational functions. Precise new error estimates for the interpolant are de- rived using existing theory for trigonometric…

Numerical Analysis · Mathematics 2014-03-12 Thomas Trogdon

A multivariate interpolation formula (MVIF) over finite fields is presented by using the proposed Kronecker delta function. The MVIF can be applied to yield polynomial relations over the base field among homogeneous symmetric rational…

Information Theory · Computer Science 2012-12-21 Yaotsu Chang , Chong-Dao Lee , Keqin Feng

We describe and analyze some Monte Carlo methods for manifolds in Euclidean space defined by equality and inequality constraints. First, we give an MCMC sampler for probability distributions defined by un-normalized densities on such…

Numerical Analysis · Mathematics 2017-09-21 Emilio Zappa , Miranda Holmes-Cerfon , Jonathan Goodman

Schubert polynomials were introduced in the context of the geometry of flag varieties. This paper investigates some of the connections not yet understood between several combinatorial structures for the construction of Schubert polynomials;…

Combinatorics · Mathematics 2007-05-23 Cristian Lenart

We show that interpolation results in the $S$-nodes theory may be considered as Khrushchev-type formulas. If separation of the well-known Verblunsky (Schur) coefficients occurs in Khrushchev formulas, the separation of the so the called new…

Classical Analysis and ODEs · Mathematics 2024-07-16 Alexander Sakhnovich

We advocate for a simple multipole expansion of the polarization density matrix. The resulting multipoles are used to construct bona fide quasiprobability distributions that appear as a sum of successive moments of the Stokes variables; the…

Quantum Physics · Physics 2013-06-04 L. L. Sanchez-Soto , A. B. Klimov , P. de la Hoz , G. Leuchs

We use Lorentz polynomials to present the solutions explicitly of equations (6.1.7) of [I. Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, 61. Society for Industrial and Applied Mathematics…

Functional Analysis · Mathematics 2015-07-14 Tian-Xiao He , Tung Nguyen

In this work we present a new WENO b-spline based quasi-interpolation algorithm. The novelty of this construction resides in the application of the WENO weights to the b-spline functions, that are a partition of unity, instead to the…

Numerical Analysis · Mathematics 2023-08-14 Sergio Amat , David Levin , Juan Ruiz-Álvarez , Dionisio F. Yáñez

In areas such as kernel smoothing and non-parametric regression there is emphasis on smooth interpolation and smooth statistical models. Splines are known to have optimal smoothness properties in one and higher dimensions. It is shown, with…

Computation · Statistics 2008-09-29 Ron A. Bates , Hugo Maruri-Aguilar , Henry P. Wynn

We are concerned with a novel Bayesian statistical framework for the characterization of natural subsurface formations, a very challenging task. Because of the large dimension of the stochastic space of the prior distribution in the…

Numerical Analysis · Mathematics 2023-02-23 Alsadig Ali , Abdullah Al-Mamun , Felipe Pereira , Arunasalam Rahunanthan

This note concerns the interpolation problem with two parametrized families of splines related to polynomial spline interpolation. We address the questions of uniqueness and establish basic convergence rates for splines of the form $…

Numerical Analysis · Mathematics 2025-09-08 Jeff Ledford , Luke Paris , Ryan Urban , Alec Vidanes

We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by…

Machine Learning · Computer Science 2014-05-20 Akshay Gadde , Aamir Anis , Antonio Ortega

We develop a constructive piecewise polynomial approximation theory in weighted Sobolev spaces with Muckenhoupt weights for any polynomial degree. The main ingredients to derive optimal error estimates for an averaged Taylor polynomial are…

Numerical Analysis · Mathematics 2014-11-27 Ricardo H. Nochetto , Enrique Otarola , Abner J. Salgado

Dual pseudo splines constitute a new class of refinable functions with B-splines as special examples, which was introduced in \cite{DHSS}. In this paper, we shall construct Riesz wavelet associated with dual pseudo splines. Furthermore, we…

Functional Analysis · Mathematics 2015-05-19 Yi Shen , Song Li

Diffusion probabilistic models (DPMs) are widely adopted for their outstanding generative fidelity, yet their sampling is computationally demanding. Polynomial-based multistep samplers mitigate this cost by accelerating inference; however,…

Machine Learning · Computer Science 2026-03-17 Soochul Park , Yeon Ju Lee , SeongJin Yoon , Jiyub Shin , Juhee Lee , Seongwoon Jo