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Steepness is a geometric property which, together with complex-analyticity, is needed in order to insure stability of a near-integrable hamiltonian system over exponentially long times. Following a strategy developed by Nekhoro-shev, we…

Dynamical Systems · Mathematics 2020-11-20 Santiago Barbieri

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire…

Optimization and Control · Mathematics 2025-04-15 Michael Muehlebach , Michael I. Jordan

We study the complexity of automatic structures via well-established concepts from both logic and model theory, including ordinal heights (of well-founded relations), Scott ranks of structures, and Cantor-Bendixson ranks (of trees). We…

Logic · Mathematics 2008-09-22 Bakhadyr Khoussainov , Mia Minnes

A bar-joint framework $(G,p)$ in Euclidean $d$-space is rigid if the only edge-length-preserving continuous motions arise from isometries of $\mathbb{R}^d$. In the generic case, rigidity is determined by the generic $d$-dimensional rigidity…

Combinatorics · Mathematics 2025-06-30 Rebecca Monks , Anthony Nixon

The relationship between overparameterization, stability, and generalization remains incompletely understood in the setting of discontinuous classifiers. We address this gap by establishing a generalization bound for finite function classes…

Machine Learning · Computer Science 2026-03-04 Jonas von Berg , Adalbert Fono , Massimiliano Datres , Sohir Maskey , Gitta Kutyniok

In this note we prove a lower bound for the rank of 2-dimensional generic rigidity matroid for regular graphs of degree four and five. Also, we give examples to show the order of the bound we give is sharp.

Combinatorics · Mathematics 2012-07-18 Shisen Luo

We here adapt an extended version of the adaptive cubic regularisation method with dynamic inexact Hessian information for nonconvex optimisation in [3] to the stochastic optimisation setting. While exact function evaluations are still…

Numerical Analysis · Mathematics 2020-09-15 Stefania Bellavia , Gianmarco Gurioli

The description complexity of a model is the length of the shortest formula that defines the model. We study the description complexity of unary structures in first-order logic FO, also drawing links to semantic complexity in the form of…

Logic · Mathematics 2024-09-27 Reijo Jaakkola , Antti Kuusisto , Miikka Vilander

We analyze the cusp anomalous dimension in the (leading) ladder limit of $\mathcal N=4$ SYM and present new results for its higher-order perturbative expansion. We study two different limits with respect to the cusp angle $\phi$. The first…

High Energy Physics - Theory · Physics 2016-06-29 Matteo Beccaria , Alberto Fachechi , Guido Macorini

We develop a theory of higher order structures in compact abelian groups. In the frame of this theory we prove general inverse theorems and regularity lemmas for Gowers's uniformity norms. We put forward an algebraic interpretation of the…

Combinatorics · Mathematics 2012-03-13 Balazs Szegedy

We address the problem, not of the determination -- which usually needs numerical methods -- but of an accurate analytical estimation of the distance of a raw elasticity tensor to cubic symmetry and to orthotropy. We point out that there…

Classical Physics · Physics 2022-06-08 Rodrigue Desmorat , Boris Kolev

This study presents a fractional-order continuum mechanics approach that allows combining selected characteristics of nonlocal elasticity, typical of classical integral and gradient formulations, under a single frame-invariant framework.…

Numerical Analysis · Mathematics 2020-05-21 Sansit Patnaik , Sai Sidhardh , Fabio Semperlotti

Fourier normal ordering \cite{Unt09bis} is a new algorithm to construct explicit rough paths over arbitrary H\"older-continuous multidimensional paths. We apply in this article the Fourier normal ordering ordering algorithm to the…

Probability · Mathematics 2009-06-08 Jeremie Unterberger

Momentum-based gradients are essential for optimizing advanced machine learning models, as they not only accelerate convergence but also advance optimizers to escape stationary points. While most state-of-the-art momentum techniques utilize…

Machine Learning · Computer Science 2025-05-20 Wei Zhang , Arif Hassan Zidan , Afrar Jahin , Yu Bao , Tianming Liu

Many complex systems satisfy a set of constraints on their degrees of freedom, and at the same time, they are able to work and adapt to different conditions. Here, we describe the emergence of this ability in a simplified model in which the…

Disordered Systems and Neural Networks · Physics 2007-05-23 Ginestra Bianconi , Roberto Mulet

We investigate a novel setting for polytope rigidity, where a flex must preserve edge lengths and the planarity of faces, but is allowed to change the shapes of faces. For instance, the regular cube is flexible in this notion. We present…

Combinatorics · Mathematics 2026-03-11 Matthias Himmelmann , Bernd Schulze , Martin Winter

Hilbert's Entscheidungsproblem has given rise to a broad and productive line of research in mathematical logic, where the classification process of decidable classes of first-order sentences represent only one of the remarkable results.…

Logic in Computer Science · Computer Science 2014-04-15 Fabio Mogavero , Giuseppe Perelli

Process flexibility is widely adopted as an effective strategy for responding to uncertain demand. Many algorithms for constructing sparse flexibility designs with good theoretical guarantees have been developed for balanced and symmetrical…

Data Structures and Algorithms · Computer Science 2018-06-11 Xi Chen , Tengyu Ma , Jiawei Zhang , Yuan Zhou

The principle of hierarchical design is a prominent theme in many natural systems where mechanical efficiency is of importance. Here we establish the properties of a particular hierarchical structure, showing that high mechanical efficiency…

Classical Physics · Physics 2013-12-18 Daniel Rayneau-Kirkhope , Yong Mao , Robert Farr

We are able to derive the equations of motion for forced mechanical systems in a purely variational setting, both in the context of Lagrangian or Hamiltonian mechanics, by duplicating the variables of the system as introduced by Galley…

Mathematical Physics · Physics 2018-08-01 D. Martín de Diego , R. Sato Martín de Almagro
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