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In this paper, we study strictly convex affine hypersurfaces centroaffinely congruent to their centre map, in the case when the shape operator has two distinct eigenvalues: one of multiplicity 1, and one nonzero of multiplicity n-1. We show…

Differential Geometry · Mathematics 2012-06-04 A. J. Vanderwinden

This paper explores recent progress related to constraint maps. Building on the exposition in [14], our goal is to provide a clear and accessible account of some of the more intricate arguments behind the main results in this work. Along…

Analysis of PDEs · Mathematics 2025-07-01 Alessio Figalli , André Guerra , Sunghan Kim , Henrik Shahgholian

One of the most basic, longstanding open problems in the theory of dynamical systems is whether reachability is decidable for one-dimensional piecewise affine maps with two intervals. In this paper we prove that for injective maps, it is…

Dynamical Systems · Mathematics 2023-03-20 Faraz Ghahremani , Edon Kelmendi , Joël Ouaknine

This work concerns the local convergence theory of Newton and quasi-Newton methods for convex-composite optimization: minimize f(x):=h(c(x)), where h is an infinite-valued proper convex function and c is C^2-smooth. We focus on the case…

Optimization and Control · Mathematics 2018-06-19 James V. Burke , Abraham Engle

We list all analytic diffeomorphisms between an open subset of the 4-dimensional projective space and an open subset of the 4-dimensional sphere that take all line segments to arcs of round circles. These are the following: restrictions of…

Differential Geometry · Mathematics 2007-05-23 Vladlen Timorin

Let $f:M^m\to N^n$ be a smooth map between two differential manifolds with $N$ connected, $f(M)$ closed and $f(M)\neq N$. In this short note, we show that either all the points of $M$ are critical points of $f$ or the dimension the…

Classical Analysis and ODEs · Mathematics 2018-05-01 Yongjie Shi , Chengjie Yu

Coupling probability measures lies at the core of many problems in statistics and machine learning, from domain adaptation to transfer learning and causal inference. Yet, even when restricted to deterministic transports, such couplings are…

Machine Learning · Statistics 2025-09-22 Lucas De Lara , Luca Ganassali

In this paper, we give a uniqueness theorem for the Dirichlet problem of minimal maps into general Riemannian manifolds with non-positive sectional curvature, improving Theorem 5.2 of Lee-Ooi-Tsui's paper published in J. Geom. Anal.. The…

Differential Geometry · Mathematics 2025-02-25 Zhiwei Jia , Minghao Li , Ling Yang

In [8,9], the authors developed a nice formula to compute the Nielsen number of a self-map on an infra-nilmanifold. For the case of nilmanifolds this formula was extended to $n$-valued maps in [4]. In this paper, we extend these results…

Algebraic Topology · Mathematics 2026-03-05 Karel Dekimpe , Lore De Weerdt

The main aim of this interdisciplinary paper is to characterize all maps on finite Minkowski space of arbitrary dimension $n$ that map pairs of distinct light-like events into pairs of distinct light-like events. Neither bijectivity of maps…

Mathematical Physics · Physics 2017-01-10 Marko Orel

Around 1960, R. Palais and J. Cerf proved a fundamental result relating spaces of diffeomorphisms and imbeddings of manifolds: If V is a submanifold of M, then the map from Diff(M) to Imb(V,M) that takes f to its restriction to V is locally…

Geometric Topology · Mathematics 2007-05-23 John Kalliongis , Darryl McCullough

This paper continues our researches \cite{DS1, DS2, DS3} by computing some invariants based on Hilbert-Poincar\'{e} series associated to Milnor algebras. Our computations are for some of the classical surfaces and 3-folds with different…

Algebraic Geometry · Mathematics 2013-10-01 Gabriel Sticlaru

The paper proves a result on the convergence of discrete conformal maps to the Riemann mappings for Jordan domains. It is a counterpart of Rodin-Sullivan's theorem on convergence of circle packing mappings to the Riemann mapping in the new…

Geometric Topology · Mathematics 2022-08-17 Feng Luo , Jian Sun , Tianqi Wu

Our original results refer to multivariate recurrences: discrete multitime diagonal recurrence, bivariate recurrence, trivariate recurrence, solutions tailored to particular situations, second order multivariate recurrences, characteristic…

Dynamical Systems · Mathematics 2015-06-16 Cristian Ghiu , Raluca Tuliga , Constantin Udriste , Ionel Tevy

A configuration of the triple $(\mathcal{P}, \mathcal{L}, \mathcal{I})$ on the incidence relation which holds the properties of "Any two points are incident with at most one line" and "Any two lines are incident with at most one point". In…

Combinatorics · Mathematics 2023-03-28 Sezer Sorgun , Ali Gökhan Ertaş , İbrahim Gunaltili

We define twelve variants of a Reifenberg's affine approximation property, which are known to be connected with the singular sets of minimal surfaces. With this motivation we investigate the regularity of the sets possessing these. We…

Metric Geometry · Mathematics 2010-12-21 Amos N. Koeller

We provide a unified approach, via deformations of incidence algebras, to several important types of representations with finiteness conditions, as well as the combinatorial algebras which produce them. We show that over finite dimensional…

Representation Theory · Mathematics 2018-05-07 Miodrag C. Iovanov , Gerard D. Koffi

It is shown that for certain maps, including concave maps, on the $d$-dimensional lattice of positive integer points, 'approximate' eigenvectors can be found. Applications in epidemiology as well as distributed resource allocation are…

Dynamical Systems · Mathematics 2018-10-30 Ohad Giladi , Björn S. Rüffer

Interesting data often concentrate on low dimensional smooth manifolds inside a high dimensional ambient space. Random projections are a simple, powerful tool for dimensionality reduction of such data. Previous works have studied bounds on…

Machine Learning · Statistics 2016-09-13 Subhaneil Lahiri , Peiran Gao , Surya Ganguli

Neural networks have been shown to have a remarkable ability to uncover low dimensional structure in data: the space of possible reconstructed images form a reduced model manifold in image space. We explore this idea directly by analyzing…

Disordered Systems and Neural Networks · Physics 2017-05-31 Lorien X. Hayden , Alexander A. Alemi , Paul H. Ginsparg , James P. Sethna