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Generalized matrix approximation plays a fundamental role in many machine learning problems, such as CUR decomposition, kernel approximation, and matrix low rank approximation. Especially with today's applications involved in larger and…

Numerical Analysis · Computer Science 2016-09-09 Haishan Ye , Qiaoming Ye , Zhihua Zhang

A new nonparametric estimator of a convex regression function in any dimension is proposed and its convergence properties are studied. We start by using any estimator of the regression function and we \emph{convexify} it by taking the…

Statistics Theory · Mathematics 2010-06-16 Néstor E. Aguilera , Liliana Forzani , Pedro Morin

In the present paper we generate binary pseudorandom sequences using generalized polynomials. A generalized polynomial is a function in whose description we not only allow addition and product (as it is the case in usual polynomials) but…

Number Theory · Mathematics 2025-09-25 Manfred G. Madritsch , Robert F. Tichy

Derivative-matching approximations are constructed as power series built from functions. The method assumes the knowledge of special values of the Bell polynomials of the second kind, for which we refer to the literature. The presented…

General Mathematics · Mathematics 2022-11-28 Andrej Liptaj

Primarily this paper presents an expository report on alternatives to the traditional methods of classifying representations of finite dimensional algebras. Some new results illustrating such alternatives for algebras with only finitely…

Representation Theory · Mathematics 2014-07-10 Birge Huisgen-Zimmermann

This is a survey article on selected topics in approximation theory. The topics either use techniques from the theory of several complex variables or arise in the study of the subject. The survey is aimed at readers having an acquaintance…

Classical Analysis and ODEs · Mathematics 2007-05-23 Norman Levenberg

This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical…

Machine Learning · Computer Science 2023-08-08 Elad Hazan

This paper explores the properties of directed graphs, termed generalized action graphs, which exhibit a strong connection to certain number sequences. Focusing on the structural and combinatorial aspects, we investigate the conditions…

Combinatorics · Mathematics 2025-07-31 Sarah Klanderman , Katy McDicken , Amelia Tebbe

Moment closure methods appear in myriad scientific disciplines in the modelling of complex systems. The goal is to achieve a closed form of a large, usually even infinite, set of coupled differential (or difference) equations. Each equation…

Statistical Mechanics · Physics 2018-12-24 Christian Kuehn

In the trace reconstruction problem, the goal is to reconstruct an unknown string $x$ of length $n$ from multiple traces obtained by passing $x$ through the deletion channel. In the relaxed problem of $approximate$ trace reconstruction, the…

Probability · Mathematics 2021-07-15 Zachary Chase , Yuval Peres

We introduce generalised orbit algebras. The purpose here is to measure how some combinatorial properties can characterize the action of a group of permutations on the subsets. The similarity with orbit algebras is such that it took the…

Combinatorics · Mathematics 2010-08-24 Xavier Buchwalder

A new general and unified method of summation, which is both regular and consistent, is invented. It is based on the idea concerning a way of integers reordering. The resulting theory includes a number of explicit and closed form summation…

Classical Analysis and ODEs · Mathematics 2011-10-26 Armen Bagdasaryan

The generalized alternating direction method of multipliers (ADMM) of Xiao et al. [{\tt Math. Prog. Comput., 2018}] aims at the two-block linearly constrained composite convex programming problem, in which each block is in the form of…

Optimization and Control · Mathematics 2022-04-05 Hongwu Li , Haibin Zhang , Yunhai Xiao

The proximal point method for a special class of nonconvex multiobjective functions is studied in this paper. We show that the method is well defined and that the accumulation points of any generated sequence, if any, are Pareto--Clarke…

Optimization and Control · Mathematics 2017-02-20 G. C. Bento , O. P. Ferreira , V. L. Sousa Junior

Compositional generalization is one of the main properties which differentiates lexical learning in humans from state-of-art neural networks. We propose a general framework for building models that can generalize compositionally using the…

Computation and Language · Computer Science 2024-02-05 Mircea Petrache , Shubhendu Trivedi

Tree convex sets refer to a collection of sets such that each set in the collection is a subtree of a tree whose nodes are the elements of these sets. They extend the concept of row convex sets each of which is an interval over a total…

Data Structures and Algorithms · Computer Science 2009-06-03 Yuanlin Zhang , Forrest Sheng Bao

We describe a novel constructive technique for devising efficient first-order methods for a wide range of large-scale convex minimization settings, including smooth, non-smooth, and strongly convex minimization. The technique builds upon a…

Optimization and Control · Mathematics 2019-06-27 Yoel Drori , Adrien B. Taylor

The author has recently introduced abstract algebraic frameworks of analogical proportions and similarity within the general setting of universal algebra. The purpose of this paper is to build a bridge from similarity to analogical…

Logic in Computer Science · Computer Science 2024-02-29 Christian Antić

In this study, the orthogonalization process for different inner products is applied to pairwise comparisons. Properties of consistent approximations of a given inconsistent pairwise comparisons matrix are examined. A method of a derivation…

Other Computer Science · Computer Science 2020-02-18 W. W. Koczkodaj , R. Smarzewski , J. Szybowski

The generalization gap of a classifier is related to the complexity of the set of functions among which the classifier is chosen. We study a family of low-complexity classifiers consisting of thresholding a random one-dimensional feature.…

Machine Learning · Computer Science 2024-09-12 Mireille Boutin , Evzenie Coupkova
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