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Manifold learning seeks a low dimensional representation that faithfully captures the essence of data. Current methods can successfully learn such representations, but do not provide a meaningful set of operations that are associated with…

Machine Learning · Computer Science 2019-08-21 David Eklund , Søren Hauberg

This article studies the smoothness of conformal mappings between two Riemannian manifolds whose metric tensors have limited regularity. We show that any bi-Lipschitz conformal mapping or $1$-quasiregular mapping between two manifolds with…

Differential Geometry · Mathematics 2016-06-06 Tony Liimatainen , Mikko Salo

We study the degree of an $L$-Lipschitz map between Riemannian manifolds, proving new upper bounds and constructing new examples. For instance, if $X_k$ is the connected sum of $k$ copies of $\mathbb CP^2$ for $k \ge 4$, then we prove that…

Metric Geometry · Mathematics 2024-10-22 Aleksandr Berdnikov , Larry Guth , Fedor Manin

We prove that if an RCD space has a regular isometric immersion in a Euclidean space, then the immersion is a locally bi-Lipschitz embedding map. This result leads us to prove that if a compact non-collapsed RCD space has an isometric…

Differential Geometry · Mathematics 2021-01-19 Shouhei Honda

We continue the study of the geometry and topology of compact submanifolds of arbitrary codimension in space forms that satisfy a pinching condition involving the length of the second fundamental form and the mean curvature. Our primary…

Differential Geometry · Mathematics 2025-09-11 Theodoros Vlachos

Given a sequence of properly embedded minimal surfaces in a $3$-manifold with local bounds on area and genus, we prove subsequential convergence, smooth away from a discrete set, to a smooth embedded limit surface, possibly with…

Differential Geometry · Mathematics 2024-01-26 Brian White

We investigate the geometry and topology of compact submanifolds of arbitrary codimension in space forms satisfying a certain pinching condition involving the length of the second fundamental form and the mean curvature. We prove that this…

Differential Geometry · Mathematics 2025-08-26 Theodoros Vlachos

The symplectic Stiefel manifold, denoted by $\mathrm{Sp}(2p,2n)$, is the set of linear symplectic maps between the standard symplectic spaces $\mathbb{R}^{2p}$ and $\mathbb{R}^{2n}$. When $p=n$, it reduces to the well-known set of $2n\times…

Optimization and Control · Mathematics 2021-07-20 Bin Gao , Nguyen Thanh Son , P. -A. Absil , Tatjana Stykel

Given a positive integer $p$, we consider $W^{1,p}$-maps from a Euclidean domain of dimension $p+1$ into a closed Riemannian manifold $\mathcal{N}$. The target manifold is required to satisfy suitable topological conditions; in particular,…

Functional Analysis · Mathematics 2026-05-28 Giacomo Canevari , Giandomenico Orlandi

Motivated by the Lipschitz rigidity problem in scalar curvature geometry, we prove that if a closed smooth spin manifold admits a distance decreasing continuous map of non-zero degree to a sphere, then either the scalar curvature is…

Differential Geometry · Mathematics 2022-07-25 Man-Chun Lee , Luen-Fai Tam

It is a classical important problem of differential topology by Thom; for a homology class of a compact manifold, can we realize this by a closed submanifold with no boundary? This is true if the degree of the class is smaller or equal to…

Algebraic Topology · Mathematics 2020-11-17 Naoki Kitazawa

This is an overview article. In his Habilitationsvortrag, Riemann described infinite dimensional manifolds parameterizing functions and shapes of solids. This is taken as an excuse to describe convenient calculus in infinite dimensions…

Differential Geometry · Mathematics 2016-04-08 Peter W. Michor

We investigate the spectra of a family of pairs (M_i,A_i) consisting of a complete Riemannian manifold M_i and a closed subset A_i and which converge in the Lipschitz topology to a pair (M,A). This is used to construct manifolds of bounded…

Differential Geometry · Mathematics 2007-05-23 K. Fissmer , U. Hamenstaedt

As our main theorem, we prove that a Lipschitz map from a compact Riemannian manifold $M$ into a Riemannian manifold $N$ admits a smooth approximation via immersions if the map has no singular points on $M$ in the sense of F.H. Clarke,…

Differential Geometry · Mathematics 2017-03-01 Kei Kondo , Minoru Tanaka

Let $n$ be a positive integer. We provide an explicit geometrically motivated $1$-Lipschitz map from the space of persistence diagrams on $n$ points (equipped with the Bottleneck distance) into the Hilbert space $\ell^2$. Such maps are a…

Metric Geometry · Mathematics 2025-10-28 Atish Mitra , Ziga Virk

This article considers the Lipschitz space with mixed logarithmic smoothness of $2\pi$ periodic functions of several variables. We obtain equivalent descriptions of the norm of the Lipschitz space and prove embedding theorems between Besov…

Classical Analysis and ODEs · Mathematics 2025-11-19 Gabdolla Akishev

Let $M^{2n}$ be a Poisson manifold with Poisson bivector field $\Pi$. We say that $M$ is b-Poisson if the map $\Pi^n:M\to\Lambda^{2n}(TM)$ intersects the zero section transversally on a codimension one submanifold $Z\subset M$. This paper…

Symplectic Geometry · Mathematics 2015-07-30 Victor Guillemin , Eva Miranda , Ana Rita Pires

Smooth manifolds are not the suitable context for trying to generalize the concept of rough paths on a manifold. Indeed, when one is working with smooth maps instead of Lipschitz maps and trying to solve a rough differential equation, one…

Classical Analysis and ODEs · Mathematics 2019-11-13 Youness Boutaib , Terry Lyons

We study the persistence of quadratic estimates related to the Kato square root problem across a change of metric on smooth manifolds by defining a class of Riemannian-like metrics that are permitted to be of low regularity and degenerate…

Analysis of PDEs · Mathematics 2019-07-04 Lashi Bandara

Many techniques in machine learning attempt explicitly or implicitly to infer a low-dimensional manifold structure of an underlying physical phenomenon from measurements without an explicit model of the phenomenon or the measurement…

Machine Learning · Statistics 2023-07-04 Roy R. Lederman , Bogdan Toader
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