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We apply the Operator Product Expansion (OPE) algorithm to the renormalization of scalar-QED theory, with a specific focus on the fixed-charge operator $\phi^Q$. Within the OPE framework, the anomalous dimension of the $\phi^Q$ operator is…

High Energy Physics - Theory · Physics 2026-05-29 Rijun Huang , Qingjun Jin , Yi Li

We show how the exact renormalization group for the effective action with a sharp momentum cutoff, may be organised by expanding one-particle irreducible parts in terms of homogeneous functions of momenta of integer degree (Taylor…

High Energy Physics - Theory · Physics 2009-10-28 Tim R. Morris

Reconstructing PDE solutions from sparse observations is a core challenge in scientific computing. We present FM4PDE, a flow-matching generative framework that learns the joint distribution of PDE coefficients (or initial states) and…

Machine Learning · Statistics 2026-05-26 Xifeng Zhang , Jin Zhao

This paper can be seen as an attempt of rethinking the {\em Extra-Gradient Philosophy} for solving Variational Inequality Problems. We show that the properly defined {\em Reduced Gradients} can be used instead for finding approximate…

Optimization and Control · Mathematics 2023-12-05 Yurii Nesterov

Performance of optimization on quadratic problems sensitively depends on the low-lying part of the spectrum. For large (effectively infinite-dimensional) problems, this part of the spectrum can often be naturally represented or approximated…

Optimization and Control · Mathematics 2024-03-26 Maksim Velikanov , Dmitry Yarotsky

The long time effect of nonlinear perturbation to oscillatory linear systems can be characterized by the averaging method, and we consider first-order averaging for its simplest applicability to high-dimensional problems. Instead of the…

Classical Analysis and ODEs · Mathematics 2018-12-05 Molei Tao

We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety of applications in machine learning and engineering, ranging from constrained deep neural networks, to…

Optimization and Control · Mathematics 2023-05-31 Ilgee Hong , Sen Na , Michael W. Mahoney , Mladen Kolar

We apply the $\delta$-expansion perturbation scheme to the $\lambda \phi^{4}$ self-interacting scalar field theory in 3+1 D at finite temperature. In the $\delta$-expansion the interaction term is written as $\lambda (\phi^{2})^{ 1 +…

High Energy Physics - Phenomenology · Physics 2007-05-23 Rudnei O. Ramos

This report addresses the boundary value problem for a second-order linear singularly perturbed FIDE. Traditional methods for solving these equations often face stability issues when dealing with small perturbation parameters. We propose an…

Numerical Analysis · Mathematics 2024-07-02 Mehebub Alam , Rajni Kant Pandey

Higher-order tree-level processes in strong laser fields, i.e. cascades, are in general extremely difficult to calculate, but in some regimes the dominant contribution comes from a sequence of first-order processes, i.e. nonlinear Compton…

High Energy Physics - Phenomenology · Physics 2020-08-05 Victor Dinu , Greger Torgrimsson

The simplicity and the efficiency of a quasi-analytical method for solving nonlinear ordinary differential equations (ODE), is illustrated on the study of anharmonic oscillators (AO) with a potential $V(x) =\beta x^{2}+x^{2m}$ ($m>0$). The…

Mathematical Physics · Physics 2011-05-03 C. Bervillier

Motivated by problems where the response is needed at select localized regions in a large computational domain, we devise a novel finite element discretization that results in exponential convergence at pre-selected points. The two key…

Numerical Analysis · Mathematics 2016-08-03 Murthy N. Guddati , Vladimir Druskin , Ali Vaziri Astaneh

We study gradient-based optimization methods obtained by direct Runge-Kutta discretization of the ordinary differential equation (ODE) describing the movement of a heavy-ball under constant friction coefficient. When the function is high…

Optimization and Control · Mathematics 2019-05-30 Jingzhao Zhang , Suvrit Sra , Ali Jadbabaie

Recent advances in cutting-plane strategies applied to robust optimization problems show that they are competitive with respect to problem reformulations and interior-point algorithms. However, although its application with polyhedral…

Optimization and Control · Mathematics 2019-04-03 Roberto Mínguez , Víctor Casero-Alonso

We propose an effectively nonperturbative approach to calculating scattering amplitudes in the perturbative regime. We do this in a discretized momentum space by using the QSE method to calculate all the contributions (to all orders in…

High Energy Physics - Phenomenology · Physics 2018-07-20 Neil Christensen , Joshua Henderson , Santiago Pinto , Cory Russ

Reduced basis approximations of Optimal Control Problems (OCPs) governed by steady partial differential equations (PDEs) with random parametric inputs are analyzed and constructed. Such approximations are based on a Reduced Order Model,…

Numerical Analysis · Mathematics 2023-08-08 Giuseppe Carere , Maria Strazzullo , Francesco Ballarin , Gianluigi Rozza , Rob Stevenson

Monotone finite difference methods provide stable convergent discretizations of a class of degenerate elliptic and parabolic Partial Differential Equations (PDEs). These methods are best suited to regular rectangular grids, which leads to…

Numerical Analysis · Mathematics 2015-11-19 Adam M. Oberman , Ian Zwiers

We propose a study of structured non-convex non-concave min-max problems which goes beyond standard first-order approaches. Inspired by the tight understanding established in recent works [Adil et al., 2022, Lin and Jordan, 2022b], we…

Optimization and Control · Mathematics 2023-04-18 Abhijeet Vyas , Brian Bullins

Recently a novel perturbative continuum limit for quantum gravity has been proposed and demonstrated to work at first order. Every interaction monomial $\sigma$ is dressed with a coefficient function $f^\sigma_\Lambda(\varphi)$ of the…

High Energy Physics - Theory · Physics 2021-04-21 Tim R. Morris

Several widely-used first-order saddle-point optimization methods yield an identical continuous-time ordinary differential equation (ODE) that is identical to that of the Gradient Descent Ascent (GDA) method when derived naively. However,…

Optimization and Control · Mathematics 2023-08-01 Tatjana Chavdarova , Michael I. Jordan , Manolis Zampetakis