Results 61 to 70 of about 522,371 (218)
Journal of Inequalities and Applications, 2016 In this paper, we construct a new family of refinable functions from generalized Bernstein polynomials, which include pseudo-splines of Type II. A comprehensive analysis of the refinable functions is carried out.
Ting Cheng, Xiaoyuan Yang
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Neural Acceleration of Graph Based Utility Functions for Sparse Matrices
IEEE Access, 2023 Many graph-based algorithms in high performance computing (HPC) use approximate solutions due to having algorithms that are computationally expensive or serial in nature.
Joshua Dennis Booth, Gregory S. Bolet
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Strong Inequalities for Hermite-Fejér Interpolations and Characterization of K-Functionals
Abstract and Applied Analysis, 2014 The works of Smale and Zhou (2003, 2007), Cucker and Smale (2002), and Cucker and Zhou (2007) indicate that approximation operators serve as cores of many machine learning algorithms.
Gongqiang You
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Streaming Algorithms for Submodular Function Maximization
, 2015 We consider the problem of maximizing a nonnegative submodular set function $f:2^{\mathcal{N}} \rightarrow \mathbb{R}^+$ subject to a $p$-matchoid constraint in the single-pass streaming setting.
A Badanidiyuru Varadaraja +16 more
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One-electron spectral functions of the attractive Hubbard model at intermediate coupling [PDF]
, 1999 We calculate the one-electron spectral function of the attractive-U Hubbard model in two dimensions. We work in the intermediate coupling and low-density regime and evaluate analytically the self energy.
M.Yu.Kagan +13 more
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Наука. Инновации. Технологии, 2022 Introduction: the methods of representation of functions given approximately by their singular integrals in relation to approximation problems and numerical methods for solving boundary value problems for differential equations are Investigated.
Igor Eduardovich Naats +2 more
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An Efficient Line Search Algorithm for Large Scale Optimization [PDF]
Al-Rafidain Journal of Computer Sciences and Mathematics, 2010 In this work we present a new algorithm of gradient descent type, in which the stepsize is computed by means of simple approximation of the Hessian Matrix to solve nonlinear unconstrained optimization function.
Abbas Al-Bayati, Ivan Latif
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Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas
, 2015 We investigate the approximability of several classes of real-valued functions by functions of a small number of variables ({\em juntas}). Our main results are tight bounds on the number of variables required to approximate a function $f:\{0,1\}^n ...
Feldman, Vitaly, Vondrak, Jan
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Algorithms, 2022 The article offers a possible treatment for the numerical research of tasks which require searching for an absolute optimum. This approach is established by employing both globalized nature-inspired methods as well as local descent methods for ...
Pavel Sorokovikov, Alexander Gornov
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Bicriteria Network Design Problems
, 1997 We study a general class of bicriteria network design problems. A generic problem in this class is as follows: Given an undirected graph and two minimization objectives (under different cost functions), with a budget specified on the first, find a
Hunt III, Harry B. +5 more
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