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We study mixed-integer programming formulations for the piecewise linear lower and upper bounds (in other words, piecewise linear relaxations) of nonlinear functions that can be modeled by a new class of combinatorial disjunctive…

Optimization and Control · Mathematics 2023-05-01 Bochuan Lyu , Illya V. Hicks , Joey Huchette

For fixed natural numbers $r$ and $s$, where $2\leq s \leq r$, we consider a representation of numbers from the interval $[0;\frac{r}{s-1}]$ obtained by encoding numbers by means of the alphabet $A=\{0,1,...,r\}$ via the expansion…

Number Theory · Mathematics 2026-01-27 M. V. Pratsiovytyi , S. P. Ratushniak , Yu. Yu. Vovk , Ya. V. Goncharenko

Motivated by an influential result of Bourgain and Tzafriri, we consider continuous matrix functions $A:\mathbb{R}\to M_{n\times n}$ and lower $\ell_2$-norm bounds associated with their restriction to certain subspaces. We prove that for…

Functional Analysis · Mathematics 2022-01-14 Adrian Fan , Jack Montemurro , Pavlos Motakis , Naina Praveen , Alyssa Rusonik , Paul Skoufranis , Noam Tobin

The analysis of complex nonlinear systems is often carried out using simpler piecewise linear representations of them. A principled and practical technique is proposed to linearize and evaluate arbitrary continuous nonlinear functions using…

Optimization and Control · Mathematics 2017-11-10 Guillermo Gallego , Daniel Berjón , Narciso García

We study universal approximation of continuous functionals on compact subsets of products of Hilbert spaces. We prove that any such functional can be uniformly approximated by models that first take finitely many continuous linear…

Machine Learning · Computer Science 2026-02-04 Andrey Krylov , Maksim Penkin

Piecewise affine functions on subsets of $\mathbb R^m$ were studied in \cite{Ovchinnikov:02,Aliprantis:06a,Aliprantis:07a,Aliprantis:07}. In this paper we study a more general concept of a locally piecewise affine function. We characterize…

Functional Analysis · Mathematics 2016-03-17 Samer Adeeb , Vladimir G. Troitsky

The linear continuity of a function defined on a vector space means that its restriction on every affine line is continuous. For functions defined on $\mathbb R^m$ this notion is near to the separate continuity for which it is required only…

General Topology · Mathematics 2020-04-09 Taras Banakh , Oleksandr Maslyuchenko

We derive fundamental lower bounds on the connectivity and the memory requirements of deep neural networks guaranteeing uniform approximation rates for arbitrary function classes in $L^2(\mathbb R^d)$. In other words, we establish a…

Machine Learning · Computer Science 2018-05-17 Helmut Bölcskei , Philipp Grohs , Gitta Kutyniok , Philipp Petersen

For every positive integer h, the representation function of order h associated to a subset A of the integers or, more generally, of any group or semigroup X, counts the number of ways an element of X can be written as the sum (or product,…

Number Theory · Mathematics 2020-04-22 Melvyn B. Nathanson

A function f:R -> R is approximately continuous iff it is continuous in the density topology, i.e., for any ordinary open set U the set E=f^{-1}(U) is measurable and has Lebesgue density one at each of its points. Denjoy proved that…

Logic · Mathematics 2016-09-06 M. Laczkovich , Arnold W. Miller

A widely used approach to compute the action $f(A)v$ of a matrix function $f(A)$ on a vector $v$ is to use a rational approximation $r$ for $f$ and compute $r(A)v$ instead. If $r$ is not computed adaptively as in rational Krylov methods,…

Numerical Analysis · Mathematics 2021-09-09 Andreas Frommer , Karsten Kahl , Manuel Tsolakis

We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data. These are functions $f$ that may be represented as the difference $\phi_1 - \phi_2$ for a choice of…

Machine Learning · Statistics 2020-11-17 Ali Siahkamari , Aditya Gangrade , Brian Kulis , Venkatesh Saligrama

A "numerical set-expression" is a term specifying a cascade of arithmetic and logical operations to be performed on sets of non-negative integers. If these operations are confined to the usual Boolean operations together with the result of…

Logic in Computer Science · Computer Science 2024-04-24 Ian Pratt-Hartmann , Ivo Düntsch

We generalize the classical Bernstein theorem concerning the constructive description of classes of functions uniformly continuous on the real line. The approximation of continuous bounded functions by entire functions of exponential type…

Complex Variables · Mathematics 2008-03-11 Vladimir Andrievskii

We give estimates for the convolution product of an arbitrary number of endlessly continuable functions. This allows us to deal with nonlinear operations for the corresponding resurgent series, e.g. substitution into a convergent power…

Dynamical Systems · Mathematics 2016-09-07 Shingo Kamimoto , David Sauzin

In this paper we study the representation theory of three monoids of partial functions on an $n$-set. The monoid of all order-preserving functions (i.e., functions satisfying $f(x)\leq f(y)$ if $x\leq y$) the monoid of all order-decreasing…

Representation Theory · Mathematics 2019-05-29 Itamar Stein

We prove that a function definable with parameters in an o-minimal structure is bounded away from infinity as its argument goes to infinity by a function definable without parameters, and that this new function can be chosen independently…

Logic · Mathematics 2011-04-22 Janak Ramakrishnan

We investigate finite sets of rational functions $\{ f_{1},f_{2}, \dots, f_{r} \}$ defined over some number field $K$ satisfying that any $t_{0} \in K$ is a $K_{p}$-value of one of the functions $f_{i}$ for almost all primes $p$ of $K$. We…

Number Theory · Mathematics 2024-08-19 Benjamin Klahn , Joachim König

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to be any…

Statistics Theory · Mathematics 2012-12-27 Arun Chandrasekhar , Victor Chernozhukov , Francesca Molinari , Paul Schrimpf

A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input. Our goal is to identify…

Machine Learning · Computer Science 2026-01-01 Alessandro Epasto , Mohammad Mahdian , Vahab Mirrokni , Manolis Zampetakis
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