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The threshold-$k$ metric dimension ($\mathrm{Tmd}_k$) of a graph is the minimum number of sensors -- a subset of the vertex set -- needed to uniquely identify any vertex in the graph, solely based on its distances from the sensors, when the…

Combinatorics · Mathematics 2021-11-18 Zsolt Bartha , Júlia Komjáthy , Järvi Raes

We consider trust-region methods for solving optimization problems where the objective is the sum of a smooth, nonconvex function and a nonsmooth, convex regularizer. We extend the global convergence theory of such methods to include…

Optimization and Control · Mathematics 2025-01-10 Minh N. Dao , Hung M. Phan , Lindon Roberts

We provide a monotone non increasing sequence of upper bounds $f^H_k$ ($k\ge 1$) converging to the global minimum of a polynomial $f$ on simple sets like the unit hypercube. The novelty with respect to the converging sequence of upper…

Optimization and Control · Mathematics 2016-05-26 Etienne de Klerk , Jean Lasserre , Monique Laurent , Zhao Sun

This article offers a comprehensive treatment of polynomial functional regression, culminating in the establishment of a novel finite sample bound. This bound encompasses various aspects, including general smoothness conditions, capacity…

Numerical Analysis · Mathematics 2024-05-08 Markus Holzleitner , Sergei Pereverzyev

For quasi-Newton methods in unconstrained minimization, it is valuable to develop methods that are robust, i.e., methods that converge on a large number of problems. Trust-region algorithms are often regarded to be more robust than…

Optimization and Control · Mathematics 2023-12-13 Johannes J Brust , Philip E Gill

We give an explicit upper bound for the algebraic degree and an explicit lower bound for the absolute value of the minimum of a polynomial function on a compact connected component of a basic closed semialgebraic set when this minimum is…

Algebraic Geometry · Mathematics 2011-12-05 Gabriela Jeronimo , Daniel Perrucci , Elias Tsigaridas

Polynomial approximations of functions are widely used in scientific computing. In certain applications, it is often desired to require the polynomial approximation to be non-negative (resp. non-positive), or bounded within a given range,…

Numerical Analysis · Mathematics 2024-11-12 Yuan Chen , Dongbin Xiu , Xiangxiong Zhang

We present computational methods for constructing orthogonal/orthonormal polynomials over arbitrary polygonal domains in $\mathbb{R}^2$ using bivariate spline functions. Leveraging a mature MATLAB implementation which generates spline…

Numerical Analysis · Mathematics 2026-01-08 Ming-Jun Lai

A general one-loop scattering amplitude may be expanded in terms of master integrals. The coefficients of the master integrals can be obtained from tree-level input in a two-step process. First, use known formulas to write the coefficients…

High Energy Physics - Phenomenology · Physics 2009-01-14 Ruth Britto , Bo Feng , Gang Yang

We prove upper bounds on the graph diameters of polytopes in two settings. The first is a worst-case bound for polytopes defined by integer constraints in terms of the height of the integers and certain subdeterminants of the constraint…

Combinatorics · Mathematics 2022-09-16 Hariharan Narayanan , Rikhav Shah , Nikhil Srivastava

We propose a symbolic-numeric algorithm to count the number of solutions of a polynomial system within a local region. More specifically, given a zero-dimensional system $f_1=\cdots=f_n=0$, with $f_i\in\mathbb{C}[x_1,\ldots,x_n]$, and a…

Symbolic Computation · Computer Science 2017-12-18 Ruben Becker , Michael Sagraloff

We identify a common scheme in several existing algorithms addressing computational problems on linear differential equations with polynomial coefficients. These algorithms reduce to computing a linear relation between vectors obtained as…

Symbolic Computation · Computer Science 2025-05-05 Louis Gaillard

The (weak) Nullstellensatz over finite fields says that if $P_1,\ldots,P_m$ are $n$-variate degree-$d$ polynomials with no common zero over a finite field $\mathbb{F}$ then there are polynomials $R_1,\ldots,R_m$ such that…

Combinatorics · Mathematics 2022-09-14 Guy Moshkovitz , Jeffery Yu

In many combinatorial problems one may need to model the diversity or similarity of assignments in a solution. For example, one may wish to maximise or minimise the number of distinct values in a solution. To formulate problems of this…

Artificial Intelligence · Computer Science 2014-01-17 Emmanuel Hebrard , Dániel Marx , Barry O'Sullivan , Igor Razgon

Provided a special function of one variable and some of its derivatives can be accurately computed over a finite range, a method is presented to build a series of polynomial approximations of the function with a defined relative error over…

Computational Physics · Physics 2007-05-23 C. Semay

We take a unifying and new approach toward polynomial and trigonometric approximation in an arbitrary number of variables, resulting in a precise and general ready-to-use tool that anyone can easily apply in new situations of interest. The…

Classical Analysis and ODEs · Mathematics 2023-05-31 Marcel de Jeu

In this paper, we compute the tightest possible bounds on the probability that the optimal value of a combinatorial optimization problem in maximization form with a random objective exceeds a given number, assuming only knowledge of the…

Optimization and Control · Mathematics 2022-11-24 Divya Padmanabhan , Selin Damla Ahipasaoglu , Arjun Ramachandra , Karthik Natarajan

Smale's alpha-theory certifies that Newton iterations will converge quadratically to a solution of a square system of analytic functions based on the Newton residual and all higher order derivatives at the given point. Shub and Smale…

Numerical Analysis · Mathematics 2016-04-06 Jonathan D. Hauenstein , Viktor Levandovskyy

We construct explicit easily implementable polynomial approximations of sufficiently high accuracy for locally constant functions on the union of disjoint segments. This problem has important applications in several areas of numerical…

Functional Analysis · Mathematics 2023-11-29 Yuri Malykhin , Konstantin Ryutin

We develop a trust-region method for minimizing the sum of a smooth term $f$ and a nonsmooth term $h$), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of $f + h$ in a trust region. The…

Optimization and Control · Mathematics 2021-08-04 Aleksandr Y. Aravkin , Robert Baraldi , Dominique Orban