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This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive…

Optimization and Control · Mathematics 2009-10-21 Silvere Bonnabel , Rodolphe Sepulchre

The stability number of a graph $G$, denoted as $\alpha(G)$, is the maximum size of an independent (stable) set in $G$. Semidefinite programming (SDP) methods, which originated from Lov\'asz's theta number and expanded through…

Optimization and Control · Mathematics 2025-09-11 Luis Felipe Vargas , Juan C. Vera , Peter J. C. Dickinson

In 2010, the first author of this paper introduced the notion of $\sigma$--stability for a nonempty subset of an $L^0(\mathcal{F},K)$--module in [T.X. Guo, Relations between some basic results derived from two kinds of topologies for a…

Functional Analysis · Mathematics 2019-04-19 Tiexin Guo , Erxin Zhang , Yachao Wang , Bixuan Yang

In this paper we introduce a new property for normed algebras. This property which we call it stability, plays a key role in the studying of the theory of almost multiplier maps. In this note we study some of the basic properties of this…

Functional Analysis · Mathematics 2015-09-29 E. Ansari Piri , S. Nouri

Geodesics become an essential element of the geometry of a semi-Riemannian manifold. In fact, their differences and similarities with the (positive definite) Riemannian case, constitute the first step to understand semi-Riemannian Geometry.…

Differential Geometry · Mathematics 2010-03-23 Anna Maria Candela , Miguel Sánchez

We show that the closed convex hull of any one-dimensional semi-algebraic subset of R^n has a semidefinite representation, meaning that it can be written as a linear projection of the solution set of some linear matrix inequality. This is…

Algebraic Geometry · Mathematics 2017-09-19 Claus Scheiderer

We study the notions generic stability, regularity, homogeneous pregeometries, quasiminimality, and their mutual relations, in an arbitrary first order theory T. We prove that "infinite-dimensional homogeneous pregeometries" coincide with…

Logic · Mathematics 2010-09-28 Anand Pillay , Predrag Tanovic

In several different settings, one comes across situations in which the objects of study are locally consistent but globally inconsistent. Earlier work about probability distributions by Vorob'ev (1962) and about database relations by…

Databases · Computer Science 2020-09-22 Albert Atserias , Phokion G. Kolaitis

This is an introduction to geometric algebra, an alternative to traditional vector algebra that expands on it in two ways: 1. In addition to scalars and vectors, it defines new objects representing subspaces of any dimension. 2. It defines…

Mathematical Physics · Physics 2012-05-29 Eric Chisolm

The class of differential equations describing pseudo-spherical surfaces, first introduced by Chern and Tenenblat [3], is characterized by the property that to each solution of a differential equation, within the class, there corresponds a…

Differential Geometry · Mathematics 2015-06-10 Nabil Kahouadji , Niky Kamran , Keti Tenenblat

Motivated by the extensive application of approximate gradients in machine learning and optimization, we investigate inexact subgradient methods subject to persistent additive errors. Within a nonconvex semialgebraic framework, assuming…

Optimization and Control · Mathematics 2025-05-14 Jérôme Bolte , Tam Le , Éric Moulines , Edouard Pauwels

In this paper, we continue studying the properties of $\gamma^{*}$-semi-open sets in topological spaces introduced by S. Hussain, B. Ahmad and T. Noiri[8]. We also introduce and discuss the $\gamma^{*}$-semi-continuous functions which…

General Topology · Mathematics 2011-03-22 Sabir Hussain , Bashir Ahmad

This work aims to extend the existing results on the Hausdorff dimension of the classical thick point sets of a Gaussian free field (GFF) to a more general class of exceptional sets. We adopt the circle or sphere averaging regularization to…

Probability · Mathematics 2018-02-12 Linan Chen

We prove the semi-Riemannian bumpy metric theorem using equivariant variational genericity. The theorem states that, on a given compact manifold $M$, the set of semi-Riemannian metrics that admit only nondegenerate closed geodesics is…

Differential Geometry · Mathematics 2014-02-26 Leonardo Biliotti , Miguel Angel Javaloyes , Paolo Piccione

This paper introduces a stratification framework for nonlinear semidefinite programming (NLSDP) that reveals and utilizes the geometry behind the nonsmooth KKT system. Based on the \emph{index stratification} of $\mathbb{S}^n$ and its lift…

Optimization and Control · Mathematics 2026-03-10 Chenglong Bao , Chao Ding , Fuxiaoyue Feng , Jingyu Li

A generalization of the classical Sard theorem in the plane is the following. Let $f$ be a function defined on a subset $A\subset{\mathbb R}^2$. If $f$ has modulus of continuity $\omega(r)\lesssim r^2$, then $f(A)\subset{\mathbb R}$ has…

Classical Analysis and ODEs · Mathematics 2025-04-10 Iqra Altaf , Marianna Csörnyei

The main purpose of this work is to provide a non-local approach to study aspects of structural stability of 3D Filippov systems. We introduce a notion of semi-local structural stability which detects when a piecewise smooth vector field is…

Dynamical Systems · Mathematics 2017-08-22 Otávio M. L. Gomide , Marco A. Teixeira

Given an embedded stable hypersurface in a four-dimensional symplectic manifold, we prove that it is stable isotopic to a $C^0$-close stable hypersurface with the following property: $C^\infty$-nearby hypersurfaces are generically unstable.…

Symplectic Geometry · Mathematics 2024-07-02 Robert Cardona

In this paper, we focus on providing convergence guarantees for stochastic subgradient methods in minimizing nonsmooth nonconvex functions. We first investigate the global stability of a general framework for stochastic subgradient methods,…

Optimization and Control · Mathematics 2024-10-15 Nachuan Xiao , Xiaoyin Hu , Kim-Chuan Toh

Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topological knowledge about the data, such as a known cluster…

Machine Learning · Computer Science 2023-11-08 Edith Heiter , Robin Vandaele , Tijl De Bie , Yvan Saeys , Jefrey Lijffijt