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We consider estimating a compact set from finite data by approximating the support function of that set via sublinear regression. Support functions uniquely characterize a compact set up to closure of convexification, and are sublinear…

Systems and Control · Electrical Eng. & Systems 2023-03-24 Shadi Haddad , Abhishek Halder

In [2] we characterized in terms of a quadratic growth condition various metric regularity properties of the subdifferential of a lower semicontinuous convex function acting in a Hilbert space. Motivated by some recent results in [16] where…

Optimization and Control · Mathematics 2015-07-01 Francisco J. Aragón Artacho , Michel H. Geoffroy

The use of convex regularizers allows for easy optimization, though they often produce biased estimation and inferior prediction performance. Recently, nonconvex regularizers have attracted a lot of attention and outperformed convex ones.…

Optimization and Control · Mathematics 2017-02-14 Quanming Yao , James. T Kwok

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

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\ell_0$ pseudo norm…

Machine Learning · Computer Science 2018-05-23 Xinyue Shen , Yuantao Gu

In this paper, we introduce a stochastic projected subgradient method for weakly convex (i.e., uniformly prox-regular) nonsmooth, nonconvex functions---a wide class of functions which includes the additive and convex composite classes. At a…

Optimization and Control · Mathematics 2018-09-19 Damek Davis , Benjamin Grimmer

The concept of convex compactness, weaker than the classical notion of compactness, is introduced and discussed. It is shown that a large class of convex subsets of topological vector spaces shares this property and that is can be used in…

Functional Analysis · Mathematics 2010-06-02 Gordan Zitkovic

The regularity theory for variational inequalities over polyhedral sets developed in a series of papers by Robinson, Ralph and Dontchev-Rockafellar in the 90s has long become classics of variational analysis. But in the available proofs of…

Optimization and Control · Mathematics 2015-09-01 Alexander D. Ioffe

The article continues the study of the 'regular' arrangement of a collection of sets near a point in their intersection. Such regular intersection or, in other words, transversality properties are crucial for the validity of qualification…

Optimization and Control · Mathematics 2018-05-15 Alexander Y. Kruger

In the early 1960's, Moreau and Rockafellar introduced a concept of called \emph{subgradient} for convex functions, initiating the developments of theoretical and applied convex analysis. The needs of going beyond convexity motivated the…

Optimization and Control · Mathematics 2013-08-06 Nguyen Mau Nam , Nguyen Dinh Hoang , R. Blake Rector

There are two basic ways of weakening the definition of the well-known metric regularity property by fixing one of the points involved in the definition. The first resulting property is called metric subregularity and has attracted a lot of…

Optimization and Control · Mathematics 2020-01-22 R. Cibulka , M. Fabian , A. Y. Kruger

We investigate a class of composite nonconvex functions, where the outer function is the sum of univariate extended-real-valued convex functions and the inner function is the limit of difference-of-convex functions. A notable feature of…

Optimization and Control · Mathematics 2024-11-21 Hanyang Li , Ying Cui

Different notions on regularity of sets and of collection of sets play an important role in the analysis of the convergence of projection algorithms in nonconvex scenarios. While some projection algorithms can be applied to feasibility…

Optimization and Control · Mathematics 2023-10-24 Rubén Campoy

Based on the needs of convergence proofs of preconditioned proximal point methods, we introduce notions of partial strong submonotonicity and partial (metric) subregularity of set-valued maps. We study relationships between these two…

Optimization and Control · Mathematics 2020-03-02 Tuomo Valkonen

Summability methods for ultraholomorphic classes in sectors, defined in terms of a strongly regular sequence $\mathbb{M}=(M_p)_{p\in\mathbb{N}_0}$, have been put forward by A. Lastra, S. Malek and the second author [1], and their validity…

Classical Analysis and ODEs · Mathematics 2018-02-16 Javier Jiménez-Garrido , Javier Sanz , Gerhard Schindl

Gopal Prasad and A. S. Rapinchuk defined a notion of weakly commensurable lattices in a semisimple group, and gave a classification of weakly commensurable Zariski dense subgroups. A motivation was to classify pairs of locally symmetric…

Number Theory · Mathematics 2012-12-07 Chandrasheel Bhagwat , Supriya Pisolkar , C. S. Rajan

Necessary and sufficient conditions for convexity and strong convexity, respectively, of sublevel sets that are defined by finitely many real-valued $C^{1,1}$-maps are presented. A novel characterization of strongly convex sets in terms of…

Optimization and Control · Mathematics 2017-01-03 Alexander Weber , Gunther Reissig

We apply the topology of convergence on compact sets to define unpredictable functions [5, 6]. The topology is metrizable and easy for applications with integral operators. To demonstrate the effectiveness of the approach, the existence and…

Chaotic Dynamics · Physics 2016-11-17 Marat Akhmet , Mehmet Onur Fen

We consider a class of sparsity-inducing regularization terms based on submodular functions. While previous work has focused on non-decreasing functions, we explore symmetric submodular functions and their \lova extensions. We show that the…

Machine Learning · Computer Science 2011-06-13 Francis Bach

In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it…

Machine Learning · Computer Science 2015-05-08 Bharath Sankaran , Marjan Ghazvininejad , Xinran He , David Kale , Liron Cohen