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We explore the relationship between convex and subharmonic functions on discrete sets. Our principal concern is to determine the setting in which a convex function is necessarily subharmonic. We initially consider the primary notions of…

Combinatorics · Mathematics 2014-06-25 Matthew Burke , Tony Perkins

We prove convergence of a single time-scale stochastic subgradient method with subgradient averaging for constrained problems with a nonsmooth and nonconvex objective function having the property of generalized differentiability. As a tool…

Optimization and Control · Mathematics 2019-12-17 Andrzej Ruszczynski

Existing analysis of AdaGrad and other adaptive methods for smooth convex optimization is typically for functions with bounded domain diameter. In unconstrained problems, previous works guarantee an asymptotic convergence rate without an…

Machine Learning · Computer Science 2023-10-05 Zijian Liu , Ta Duy Nguyen , Alina Ene , Huy L. Nguyen

Level proximal subdifferential was introduced by Rockafellar recently for studying proximal mappings of possibly nonconvex functions. In this paper a systematic study of level proximal subdifferential is given. We characterize variational…

Optimization and Control · Mathematics 2026-04-22 Honglin Luo , Xianfu Wang , Ziyuan Wang , Xinmin Yang

We extend the duality principle for the $\Gamma$-convergence of convex lower semicontinuous functions, which was previously established only in separable reflexive Banach spaces, to the broader class of weakly compactly generated (WCG)…

Functional Analysis · Mathematics 2026-05-14 Rafael Correa , Pedro Pérez-Aros , José Pablo Santander

We show that Caratheodory's conjecture, on umbilical points of closed convex surfaces, may be reformulated in terms of the existence of at least one umbilic in the graphs of functions f: R^2-->R whose gradient decays uniformly faster than…

Differential Geometry · Mathematics 2011-08-30 Mohammad Ghomi , Ralph Howard

In a nutshell, submodular functions encode an intuitive notion of diminishing returns. As a result, submodularity appears in many important machine learning tasks such as feature selection and data summarization. Although there has been a…

Data Structures and Algorithms · Computer Science 2018-03-19 Marko Mitrovic , Moran Feldman , Andreas Krause , Amin Karbasi

In this paper, we deal with a notion of Banach space-valued mappings defined on a set consisting of finite graphs with uniformly bounded vertex degree. These functions will be endowed with certain boundedness and additivity criteria. We…

Combinatorics · Mathematics 2015-01-14 Felix Pogorzelski

For the minimal graph with strict convex level sets, we find an auxiliary function to study the Gaussian curvature of the level sets. We prove that this curvature function is a concave function with respect to the height of the minimal…

Analysis of PDEs · Mathematics 2016-01-20 Pei-He Wang

We propose a level proximal subdifferential for a proper lower semicontinuous function. Level proximal subdifferential is a uniform refinement of the well-known proximal subdifferential, and has the pleasant feature that its resolvent…

Optimization and Control · Mathematics 2023-03-07 Xianfu Wang , Ziyuan Wang

Let $\mathcal{G} = \{G_1 = (V, E_1), \dots, G_m = (V, E_m)\}$ be a collection of $m$ graphs defined on a common set of vertices $V$ but with different edge sets $E_1, \dots, E_m$. Informally, a function $f :V \rightarrow \mathbb{R}$ is…

Spectral Theory · Mathematics 2022-03-03 Ronald R. Coifman , Nicholas F. Marshall , Stefan Steinerberger

This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity? We prove that the stochastic subgradient method, on any semialgebraic locally Lipschitz…

Optimization and Control · Mathematics 2018-05-29 Damek Davis , Dmitriy Drusvyatskiy , Sham Kakade , Jason D. Lee

Gradient normalization and soft clipping are two popular techniques for tackling instability issues and improving convergence of stochastic gradient descent (SGD) with momentum. In this article, we study these types of methods through the…

Optimization and Control · Mathematics 2025-07-01 Måns Williamson , Tony Stillfjord

The paper investigates the approximation of the symmetric Total Variation functional on graphs. Such an approximation is given in terms of a discrete and symmetric finite difference model defined on point clouds obtained by randomly…

Analysis of PDEs · Mathematics 2026-03-31 Stefano Almi , Anna Kubin , Emanuele Tasso

In discrete convex analysis, the scaling and proximity properties for the class of L$^\natural$-convex functions were established more than a decade ago and have been used to design efficient minimization algorithms. For the larger class of…

Combinatorics · Mathematics 2017-12-13 Satoko Moriguchi , Kazuo Murota , Akihisa Tamura , Fabio Tardella

In this article, we prove an extension of the mean value theorem and a comparison theorem for subharmonic functions. These theorems are used to answer the question whether we can conclude that two subharmonic functions which agree almost…

Complex Variables · Mathematics 2019-09-24 Thai-Duong Do

We initiate a program of average smoothness analysis for efficiently learning real-valued functions on metric spaces. Rather than using the Lipschitz constant as the regularizer, we define a local slope at each point and gauge the function…

Statistics Theory · Mathematics 2020-11-10 Yair Ashlagi , Lee-Ad Gottlieb , Aryeh Kontorovich

The existence of a homogeneous decomposition for continuous and epi-translation invariant valuations on super-coercive functions is established. Continuous and epi-translation invariant valuations that are epi-homogeneous of degree $n$ are…

Metric Geometry · Mathematics 2020-05-15 A. Colesanti , M. Ludwig , F. Mussnig

Active contour models based on partial differential equations have proved successful in image segmentation, yet the study of their geometric formulation on arbitrary geometric graphs is still at an early stage. In this paper, we introduce…

Computer Vision and Pattern Recognition · Computer Science 2016-10-25 Christos Sakaridis , Kimon Drakopoulos , Petros Maragos

Abstract convexity generalises classical convexity by considering the suprema of functions taken from an arbitrarily defined set of functions. These are called the abstract linear (abstract affine) functions. The purpose of this paper is to…

Optimization and Control · Mathematics 2025-01-30 Reinier Diàz Millàn , Nadezda Sukhorukova , Julien Ugon