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A key concern in modern distributed systems is to avoid the cost of coordination while maintaining consistent semantics. Until recently, there was no answer to the question of when coordination is actually required. In this paper we present…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-01-29 Joseph M. Hellerstein , Peter Alvaro

The complexity class $NP$ can be logically characterized both through existential second order logic $SO\exists$, as proven by Fagin, and through simulating a Turing machine via the satisfiability problem of propositional logic SAT, as…

Logic · Mathematics 2014-10-21 Tuomo Kauranne

The homology groups of many natural sequences of groups $\{G_n\}_{n=1}^{\infty}$ (e.g. general linear groups, mapping class groups, etc.) stabilize as $n \rightarrow \infty$. Indeed, there is a well-known machine for proving such results…

Algebraic Topology · Mathematics 2017-02-22 Andrew Putman

Let $G$ be a graph with $n$ vertices and $m$ edges. One of several hierarchies towards the stability number of $G$ is the exact subgraph hierarchy (ESH). On the first level it computes the Lov\'{a}sz theta function $\vartheta(G)$ as…

Optimization and Control · Mathematics 2024-06-05 Elisabeth Gaar

Let $F$ be a non-archimedean local field. Let $\overline{F}$ be an algebraic closure of $F$. Let $G$ be a connected reductive group over $F$. Let $\varphi$ be an elliptic $L$-parameter. For every irreducible representation $\pi$ of $G(F)$…

Representation Theory · Mathematics 2025-01-03 Chenji Fu

We construct analogues of FI-modules where the role of the symmetric group is played by the general linear groups and the symplectic groups over finite rings and prove basic structural properties such as Noetherianity. Applications include…

Algebraic Topology · Mathematics 2017-10-18 Andrew Putman , Steven V Sam

We show how to compose robust stability tests for uncertain systems modeled as linear fractional representations and affected by various types of dynamic uncertainties. Our results are formulated in terms of linear matrix inequalities and…

Optimization and Control · Mathematics 2022-10-19 Tobias Holicki , Carsten W. Scherer

This paper studies the problem of safe stabilization of control-affine systems under uncertainty. Our starting point is the availability of worst-case or probabilistic error descriptions for the dynamics and a control barrier function…

Optimization and Control · Mathematics 2023-12-05 Pol Mestres , Jorge Cortés

We generalize the stable graph regularity lemma of Malliaris and Shelah to the case of finite structures in finite relational languages, e.g., finite hypergraphs. We show that under the model-theoretic assumption of stability, such a…

Logic · Mathematics 2018-01-16 Nathanael Ackerman , Cameron Freer , Rehana Patel

We study the problem of co-designing control barrier functions and linear state feedback controllers for discrete-time linear systems affected by additive disturbances. For disturbances of bounded magnitude, we provide a semi-definite…

Optimization and Control · Mathematics 2025-05-14 Marta Fochesato , Han Wang , Antonis Papachristodoulou , Paul Goulart

The purpose of this paper is to give a sufficient condition for (strong) stability of non-proper smooth functions (with respect to the Whitney $C^\infty$-topology). We show that a Morse function is stable if it is end-trivial at any point…

Geometric Topology · Mathematics 2021-04-19 Kenta Hayano

In this work, we present a numerical optimal control framework for reachable set computation using \emph{normotopes}, a new set representation as a norm ball with a shaping matrix. In reachable set computations, we expect to continuously…

Optimization and Control · Mathematics 2025-09-30 Akash Harapanahalli , Samuel Coogan

This work primarily focuses on an operator inference methodology aimed at constructing low-dimensional dynamical models based on a priori hypotheses about their structure, often informed by established physics or expert insights. Stability…

Machine Learning · Computer Science 2024-03-04 Igor Pontes Duff , Pawan Goyal , Peter Benner

Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness. Moreover, they do not account for sparsity in the input…

Machine Learning · Computer Science 2023-02-28 Aleksandar Bojchevski , Johannes Gasteiger , Stephan Günnemann

We introduce a relaxation of stability, called almost sure stability, which is insensitive to perturbations by subsets of Loeb measure $0$ in a non-standard finite group. We show that almost sure stability satisfies a stationarity principle…

Logic · Mathematics 2026-01-14 Amador Martin-Pizarro , Daniel Palacin , Julia Wolf

The main problem in the area of graph property testing is to understand which graph properties are \emph{testable}, which means that with constantly many queries to any input graph $G$, a tester can decide with good probability whether $G$…

Data Structures and Algorithms · Computer Science 2022-05-04 Louis Esperet , Sergey Norin

In this paper we introduce the notion of the stability of a sequence of modules over Hecke algebras. We prove that a finitely generated consistent sequence associated with Hecke algebras is representation stable.

Representation Theory · Mathematics 2018-02-05 Kun Wang , Haitao Ma , Zhu-Jun Zheng

A \emph{new} notion of equilibrium, which we call \emph{strong equilibrium}, is introduced for time-inconsistent stopping problems in continuous time. Compared to the existing notions introduced in ArXiv: 1502.03998 and ArXiv: 1709.05181,…

Mathematical Finance · Quantitative Finance 2020-10-02 Erhan Bayraktar , Jingjie Zhang , Zhou Zhou

Graph neural networks (GNNs) have recently been demonstrated to perform well on a variety of network-based tasks such as decentralized control and resource allocation, and provide computationally efficient methods for these tasks which have…

Machine Learning · Computer Science 2021-12-15 Raghu Arghal , Eric Lei , Shirin Saeedi Bidokhti

Recovery procedures in various application in Data Science are based on \emph{stable point separation}. In its simplest form, stable point separation implies that if $f$ is "far away" from $0$, and one is given a random sample…

Probability · Mathematics 2019-04-19 Shahar Mendelson , Grigoris Paouris
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