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Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications. They have been used in many areas, including combinatorial optimization, machine learning, and economics. In…

Data Structures and Algorithms · Computer Science 2012-08-24 Maria-Florina Balcan , Nicholas J. A. Harvey

The purpose of this paper is to study the lower semicontinuity with respect to the strong $L^1$-convergence, of some integral functionals defined in the space SBD of special functions with bounded deformation. Precisely, let $U$ be a…

Functional Analysis · Mathematics 2007-05-23 Francois Ebobisse

This paper is devoted to the study of generalized differentiation properties of the infimal convolution. This class of functions covers a large spectrum of nonsmooth functions well known in the literature. The subdifferential formulas…

Optimization and Control · Mathematics 2014-11-04 Nguyen Mau Nam , Dang Van Cuong

Submodular set-functions have many applications in combinatorial optimization, as they can be minimized and approximately maximized in polynomial time. A key element in many of the algorithms and analyses is the possibility of extending the…

Machine Learning · Computer Science 2016-02-24 Francis Bach

In this paper it is shown that higher order quasiconvex functions suitable in the variational treatment of problems involving second derivatives may be extended to the space of all matrices as classical quasiconvex functions. Precisely, it…

Analysis of PDEs · Mathematics 2009-11-10 Gianni Dal Maso , Irene Fonseca , Giovanni Leoni , Massimiliano Morini

A differentiable function is pseudoconvex if and only if its restrictions over straight lines are pseudoconvex. A differentiable function depending on one variable, defined on some closed interval $[a,b]$ is pseudoconvex if and only if…

Optimization and Control · Mathematics 2019-11-19 Vsevolod Ivanov Ivanov

We begin with an improvement to an extension result for subharmonic functions of Blanchet et al. With the aid of this improvement we then give extension results for subharmonic functions, for separately subharmonic functions, for harmonic…

Analysis of PDEs · Mathematics 2019-07-22 Juhani Riihentaus

This paper is devoted to the approximation of differentiable semialgebraic functions by Nash functions. Approximation by Nash functions is known for semialgebraic functions defined on an affine Nash manifold M, and here we extend it to…

Algebraic Geometry · Mathematics 2013-07-03 Elías Baro , José F. Fernando , Jesús M. Ruiz

Classical results show that gradient descent converges linearly to minimizers of smooth strongly convex functions. A natural question is whether there exists a locally nearly linearly convergent method for nonsmooth functions with quadratic…

Optimization and Control · Mathematics 2023-07-18 Damek Davis , Liwei Jiang

We apply upper and lower compensated convex transforms, which are `tight' one-sided approximations of a given function, to the extraction of fine geometric singularities from semiconvex/semiconcave functions and DC-functions in…

Optimization and Control · Mathematics 2016-10-06 Kewei Zhang , Elaine Crooks , Antonio Orlando

Spectral functions of symmetric matrices -- those depending on matrices only through their eigenvalues -- appear often in optimization. A cornerstone variational analytic tool for studying such functions is a formula relating their…

Optimization and Control · Mathematics 2015-07-23 D. Drusvyatskiy , C. Kempton

We investigate semiconjugate rational functions, that is rational functions $A,$ $B$ related by the functional equation $A\circ X=X\circ B$, where $X$ is a rational function of degree at least two. We show that if $A$ and $B$ is a pair of…

Dynamical Systems · Mathematics 2016-08-17 F. Pakovich

We prove that the gradient of any bounded subharmonic function is upper semi-continuous, provided that its super-level sets can be touched from the exterior by uniform $C^{1,\text{Dini}}$ domains at every point. This idea extends to a class…

Analysis of PDEs · Mathematics 2026-02-18 Aram Hakobyan , Michael Poghosyan , Henrik Shahgholian

A fundamental open question asking whether all real-valued strongly quasiconvex functions defined on $\mathbb R^n$ are necessarily continuous, akin to their convex counterparts, is answered in detail in this paper. Among other things, we…

Optimization and Control · Mathematics 2025-12-04 Nguyen Thi Van Hang , Felipe Lara , Nguyen Dong Yen

We identity the optimal non-infinitesimal direction of descent for a convex function. An algorithm is developed that can theoretically minimize a subset of (non-convex) functions.

Optimization and Control · Mathematics 2025-09-19 Andrew J. Young

The paper concerns the investigation of nonconvex and nondifferentiable integral functionals on general Banach spaces, which may not be reflexive and/or separable. Considering two major subdifferentials of variational analysis, we derive…

Optimization and Control · Mathematics 2016-03-28 Boris S. Mordukhovich , Nobusumi Sagara

We generalize and improve the original characterization given by Valadier [20, Theorem 1] of the subdifferential of the pointwise supremum of convex functions, involving the subdifferentials of the data functions at nearby points. We remove…

Optimization and Control · Mathematics 2017-07-13 R. Correa , A. Hantoute , M. A. López

In the first part, we discuss the stability of the strong slope and of the subdifferential of a lower semicontinuous function with respect to Wijsman perturbations of the function, i.e. perturbations described via Wijsman convergence. In…

Optimization and Control · Mathematics 2019-01-01 Marc Lassonde

We provide new necessary and sufficient conditons for ensuring strong quasiconvexity in the nonsmooth case and, as a consequence, we provide a proof for the differentiable case. Furthermore, we improve the quadratic growth property for…

Optimization and Control · Mathematics 2025-09-29 Nicolas Hadjisavvas , Felipe Lara

This note deals with certain properties of convex functions. We provide results on the convexity of the set of minima of these functions, the behaviour of their subgradient set under restriction, and optimization of these functions over an…

Optimization and Control · Mathematics 2017-03-21 Miel Sharf , Daniel Zelazo