Latest Research

Flow updates for domain decomposition of entropic optimal transport

otgroupgoe/domaindecomposition 15 May 2024

In practice this can be avoided by a coarse-to-fine multiscale scheme.

Numerical Analysis Numerical Analysis

15 May 2024

Unveiling low-dimensional patterns induced by convex non-differentiable regularizers

ivanhejny/pyslope 13 May 2024

While the asymptotic distribution of the rescaled estimation error can be derived by relatively standard arguments, the convergence of the pattern does not simply follow from the convergence in distribution, and requires a careful and separate treatment.

Statistics Theory Statistics Theory

13 May 2024

Cloaking for random walks using a discrete potential theory

fguevaravas/crwdpt 13 May 2024

In addition to these capabilities, the active strategy can also hide sources of particles, at the cost of prior knowledge of the expected net particle charges in the reference graph.

Probability Numerical Analysis Numerical Analysis 05C81, 31C20, 65M80, 31B10

13 May 2024

Can Neural Networks learn Finite Elements?

EduardoTerres/Can-Neural-Networks-learn-Finite-Elements 10 May 2024

The aim of this note is to construct a neural network for which the linear finite element approximation of a simple one dimensional boundary value problem is a minimum of the cost function to find out if the neural network is able to reproduce the finite element approximation.

Numerical Analysis Numerical Analysis

10 May 2024

Some uniform effective results on André--Oort for sums of powers in $\mathbb{C}^n$

guyfowler/sums_of_powers 10 May 2024

We prove an Andr\'e--Oort-type result for a family of hypersurfaces in $\mathbb{C}^n$ that is both uniform and effective.

Number Theory 11G18, 14G35

10 May 2024

Backward errors for multiple eigenpairs in structured and unstructured nonlinear eigenvalue problems

miryamgnazzo/backward-error-nonlinear 10 May 2024

Given a nonlinear matrix-valued function $F(\lambda)$ and approximate eigenpairs $(\lambda_i, v_i)$, we discuss how to determine the smallest perturbation $\delta F$ such that $[F + \delta F](\lambda_i) v_i = 0$; we call the distance between the $F$ and $F + \delta F$ the backward error for this set of approximate eigenpairs.

Numerical Analysis Numerical Analysis 65H17, 65F15, 15A18

10 May 2024

Estimation of ill-conditioned models using penalized sums of squares of the residuals

rnoremlas/penalized-estimator 9 May 2024

This paper analyzes the estimation of econometric models by penalizing the sum of squares of the residuals with a factor that makes the model estimates approximate those that would be obtained when considering the possible simple regressions between the dependent variable of the econometric model and each of its independent variables.

Statistics Theory Statistics Theory 62J05

09 May 2024

Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs

secquoia/dsda-gdp 8 May 2024

This paper presents the Logic-based Discrete-Steepest Descent Algorithm (LD-SDA) as a solution method for GDP problems involving ordered Boolean variables.

Optimization and Control

08 May 2024

Randomized iterative methods for generalized absolute value equations: Solvability and error bounds

xiejx-math/GAVE-codes 7 May 2024

Randomized iterative methods, such as the Kaczmarz method and its variants, have gained growing attention due to their simplicity and efficiency in solving large-scale linear systems.

Numerical Analysis Numerical Analysis

07 May 2024

Fibonacci Neural Network Approach for Numerical Solutions of Fractional Order Differential Equations

Kushaldhardwivedi/Differential-Equation-articles 7 May 2024

In this paper, the authors propose the utilization of Fibonacci Neural Networks (FNN) for solving arbitrary order differential equations.

Number Theory

07 May 2024