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We initiate the theory of graded commutative 2-rings, a categorification of graded commutative rings. The goal is to provide a systematic generalization of Paul Balmer's comparison maps between the spectrum of tensor-triangulated categories…

Category Theory · Mathematics 2016-05-11 Ivo Dell'Ambrogio , Greg Stevenson

The matrix spectral and nuclear norms appear in enormous applications. The generalizations of these norms to higher-order tensors is becoming increasingly important but unfortunately they are NP-hard to compute or even approximate. Although…

Optimization and Control · Mathematics 2023-03-01 Simai He , Haodong Hu , Bo Jiang , Zhening Li

We present a stochastic optimization method that uses a fourth-order regularized model to find local minima of smooth and potentially non-convex objective functions with a finite-sum structure. This algorithm uses sub-sampled derivatives…

Optimization and Control · Mathematics 2023-07-18 Aurelien Lucchi , Jonas Kohler

This paper discusses our recent generalized optimal algebraic multigrid (AMG) convergence theory applied to the steady-state Stokes equations discretized using Taylor-Hood elements ($\pmb{ \mathbb{P}}_2/\mathbb{P}_{1}$). The generalized…

In this paper, we show that Goodwillie calculus, as applied to functors from stable homotopy to itself, interacts in striking ways with chromatic aspects of the stable category. Localized at a fixed prime p, let T(n) be the telescope of a…

Algebraic Topology · Mathematics 2015-06-26 Nicholas J. Kuhn

Tensor train (TT) factorization and corresponding TT rank, which can well express the low-rankness and mode correlations of higher-order tensors, have attracted much attention in recent years. However, TT factorization based methods are…

Image and Video Processing · Electrical Eng. & Systems 2022-05-09 Gaohang Yu , Shaochun Wan , Liqun Qi , Yanwei Xu

Tensor completion is a core machine learning algorithm used in recommender systems and other domains with missing data. While the matrix case is well-understood, theoretical results for tensor problems are limited, particularly when the…

Machine Learning · Statistics 2023-06-13 Kameron Decker Harris , Oscar López , Angus Read , Yizhe Zhu

We develop a categorical and algebro-geometric treatment of localization for cohomological theories endowed with an open--closed recollement. Starting from a class on a space whose restriction to the open complement vanishes, we show that…

Algebraic Geometry · Mathematics 2026-04-09 Mauricio Corrêa , Simone Noja

In this master thesis, we extend results from classical simple homotopy theory to the world of stratified homotopy theory. To obtain a well-established framework to work in, we prove a series of results on two model categories of simplicial…

Algebraic Topology · Mathematics 2021-02-16 Lukas Waas

We present an efficient low-rank approximation algorithm for non-negative tensors. The algorithm is derived from our two findings: First, we show that rank-1 approximation for tensors can be viewed as a mean-field approximation by treating…

Machine Learning · Statistics 2021-10-26 Kazu Ghalamkari , Mahito Sugiyama

We introduce the $\star_G$ tensor algebra, in which any finite group $G$ defines the multiplication rule, making equivariance an intrinsic algebraic property rather than an architectural constraint. The framework rests on three…

Grassmann tensors arise from classical problems of scene reconstruction in computer vision. Trifocal Grassmann tensors, related to three projections from a projective space of dimension k onto view-spaces of varying dimensions are studied…

Algebraic Geometry · Mathematics 2019-07-25 Marina Bertolini , Gian Mario Besana , Gilberto Bini , Cristina Turrini

We solve the $K$-theoretically refined Donaldson-Thomas theory of local curves. Our results avoid degeneration techniques, but rather exploit direct localisation methods to reduce the refined Donaldson-Thomas partition function to the…

Algebraic Geometry · Mathematics 2026-04-08 Sergej Monavari

Tensor regression has attracted significant attention in statistical research. This study tackles the challenge of handling covariates with smooth varying structures. We introduce a novel framework, termed functional tensor regression,…

Methodology · Statistics 2025-06-12 Tongyu Li , Fang Yao , Anru R. Zhang

We study the equivariant generalization of topological strings on toric manifolds, focusing in particular on defining the contributions of constant maps in the genus expansion of the partition function. This approach regularizes the…

High Energy Physics - Theory · Physics 2025-12-05 Luca Cassia , Kiril Hristov

In the study of equisingularity of families of mappings Gaffney introduced the crucial notion of excellent unfoldings. This definition essentially says that the family can be stratified so that there are no strata of dimension 1 other than…

Algebraic Geometry · Mathematics 2008-07-03 Kevin Houston

Using the six-functor formalism for sheaves of spectra on topological spaces, we provide a novel construction of the Bauer--Furuta invariant, as well as its family version. This approach avoids the conventional arguments based on…

Algebraic Topology · Mathematics 2024-12-24 Takumi Maegawa

Scalar-tensor theories are one of the most natural and well-constrained alternative theories of gravity, while still allowing for significant deviations from general relativity. We present the equations of motion of nonspinning compact…

General Relativity and Quantum Cosmology · Physics 2023-03-02 Laura Bernard

In this article, we show that the localization of an extriangulated category by a multiplicative system satisfying mild assumptions can be equipped with a natural, universal structure of an extriangulated category. This construction unifies…

Category Theory · Mathematics 2021-06-17 Hiroyuki Nakaoka , Yasuaki Ogawa , Arashi Sakai

Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-transition matrix. We propose a new approximation which lies on…

Machine Learning · Computer Science 2019-06-03 Daniele Castellana , Davide Bacciu