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We consider the problem of estimating an unknown function f* and its partial derivatives from a noisy data set of n observations, where we make no assumptions about f* except that it is smooth in the sense that it has square integrable…

Machine Learning · Statistics 2024-05-17 Eunji Lim

This paper identifies necessary and sufficient conditions for the exactness of penalty functions in optimization problems whose constraint sets are not necessarily bounded. The case where the data of problems is locally Lipschitz,…

Optimization and Control · Mathematics 2025-10-21 Liguo Jiao , Tien-Son Pham , Nguyen Van Tuyen

This paper investigates a specific class of nonsmooth nonconvex optimization problems in the face of data uncertainty, namely, robust optimization problems, where the given objective function can be expressed as a difference of two…

Optimization and Control · Mathematics 2026-02-20 Feryal Mashkoorzadeh , Nooshin Movahedian

We study a class of semilinear diffusion equations on infinite, connected, weighted graphs, focusing on two types of nonlinearities: monotone decreasing and Lipschitz continuous. Under minimal structural assumptions on the graph, we…

Analysis of PDEs · Mathematics 2026-05-15 Elvise Berchio , Davide Bianchi , Alberto G. Setti , Maria Vallarino

We study the local Lipschitz one subsets of a finite dimensional space, that is, sets for which there exists a continuous function whose local Lipschitz derivative is the characteristic function of said set. We give a characterization of a…

Functional Analysis · Mathematics 2026-04-22 Ziemowit M. Wójcicki

In this paper we develop general formulas for the subdifferential of the pointwise supremum of convex functions, which cover and unify both the compact continuous and the non-compact non-continuous settings. From the non-continuous to the…

Optimization and Control · Mathematics 2020-04-03 Rafael Correa , Abderrahim Hantoute , Marco Antonio López

Upper semicontinuous (usc) functions arise in the analysis of maximization problems, distributionally robust optimization, and function identification, which includes many problems of nonparametric statistics. We establish that every usc…

Optimization and Control · Mathematics 2019-07-09 Johannes O. Royset

We develop a nonparametric approach to identify and estimate consumer preferences and unobserved heterogeneity under nonlinear price schedules. Leveraging variation across multiple price schedules, we show that both the utility function and…

Econometrics · Economics 2026-04-29 Samuele Centorrino , Frédérique Fève , Jean-Pierre Florens

We consider a class of functions defined on metric spaces which generalizes the concept of piecewise Lipschitz continuous functions on an interval or on polyhedral structures. The study of such functions requires the investigation of their…

General Topology · Mathematics 2021-12-14 Gunther Leobacher , Alexander Steinicke

The convergence theory for the gradient sampling algorithm is extended to directionally Lipschitz functions. Although directionally Lipschitz functions are not necessarily locally Lipschitz, they are almost everywhere differentiable and…

Optimization and Control · Mathematics 2021-07-13 James V. Burke , Qiuying Lin

Particles on Demand formulation of kinetic theory [B. Dorschner, F. B\"{o}sch and I. V. Karlin, {\it Phys. Rev. Lett.} {\bf 121}, 130602 (2018)] is used to simulate a variety of compressible flows with strong discontinuities in density,…

Fluid Dynamics · Physics 2022-07-20 N. G. Kallikounis , B. Dorschner , I. V. Karlin

We show that for every complete metric space $M$ there exists another complete metric space $N$ of the same density character such that the curve-flat quotient of $N$ is isometric to $M$. Moreover, we show that if $M$ is compact and…

Metric Geometry · Mathematics 2026-03-23 Jaan Kristjan Kaasik , Andrés Quilis

This paper presents a tractable algorithm for estimating an unknown Lipschitz function from noisy observations and establishes an upper bound on its convergence rate. The approach extends max-affine methods from convex shape-restricted…

Machine Learning · Statistics 2025-11-20 Gábor Balázs

Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation…

Machine Learning · Computer Science 2019-05-07 Andrew An Bian , Baharan Mirzasoleiman , Joachim M. Buhmann , Andreas Krause

We initiate the study of nonsmooth optimization problems under bounded local subgradient variation, which postulates bounded difference between (sub)gradients in small local regions around points, in either average or maximum sense. The…

Optimization and Control · Mathematics 2024-11-05 Jelena Diakonikolas , Cristóbal Guzmán

We study theoretical and computational properties of the pressure function for subshifts of finite type on the integer lattice $\Z^d$, multidimensional SOFT, which are called Potts models in mathematical physics. We show that the pressure…

Mathematical Physics · Physics 2010-05-19 Shmuel Friedland , Uri N. Peled

Submodular set functions are undoubtedly among the most important building blocks of combinatorial optimization. Somewhat surprisingly, continuous counterparts of such functions have also appeared in an analytic line of research where they…

Combinatorics · Mathematics 2024-06-10 Kristóf Bérczi , Boglárka Gehér , András Imolay , László Lovász , Tamás Schwarcz

We provide a new, short proof of the density in energy of Lipschitz functions into the metric Sobolev space defined by using plans with barycenter (and thus, a fortiori, into the Newtonian-Sobolev space). Our result covers first-order…

Functional Analysis · Mathematics 2024-02-02 Danka Lučić , Enrico Pasqualetto

If we consider a sequence of warped product length spaces, what conditions on the sequence of warping functions implies compactness of the sequence of distance functions? In particular, we want to know when a subsequence converges to a well…

Differential Geometry · Mathematics 2024-09-12 Brian Allen , Bryan Sanchez , Yahaira Torres

The self-concordant-like property of a smooth convex function is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self-concordant-like property, this concept…

Optimization and Control · Mathematics 2018-01-23 Quoc Tran-Dinh , Yen-Huan Li , Volkan Cevher