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Continuous DR-submodular functions are a class of functions that satisfy the Diminishing Returns (DR) property, which implies that they are concave along non-negative directions. Existing works have studied monotone continuous DR-submodular…

Machine Learning · Computer Science 2022-05-31 Omid Sadeghi , Maryam Fazel

Let C be a reduced, irreducible, not degenerate curve, not contained on surfaces of degree <s; when d=deg(C) is large with respect to s, the arithmetic genus p_a(c) is bounded by a function G(d, r, s) which is of type d^2/2s+O(d). The…

Algebraic Geometry · Mathematics 2007-05-23 Rita Ferraro

We prove that maximal operators of convolution type associated to smooth kernels are bounded in the homogeneous Hardy-Sobolev spaces $\dot{H}^{1,p}(\mathbb{R}^d)$ when $1/p < 1+1/d$. This range of exponents is sharp. As a by-product of the…

Classical Analysis and ODEs · Mathematics 2021-02-23 Carlos Pérez , Tiago Picon , Olli Saari , Mateus Sousa

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

By studying $\mathbb{A}^1$-curves on varieties, we propose a geometric approach to strong approximation problem over function fields of complex curves. We prove that strong approximation holds for smooth, low degree affine complete…

Algebraic Geometry · Mathematics 2015-10-16 Qile Chen , Yi Zhu

The problem of maximizing non-negative submodular functions has been studied extensively in the last few years. However, most papers consider submodular set functions. Recently, several advances have been made for the more general case of…

Discrete Mathematics · Computer Science 2016-11-29 Corinna Gottschalk , Britta Peis

We study the pointwise supremum of convex integral functionals $\mathcal{I}_{f,\gamma}(\xi)= \sup_{Q} \left( \int_\Omega f(\omega,\xi(\omega))Q(d\omega)-\gamma(Q)\right)$ on $L^\infty(\Omega,\mathcal{F},\mathbb{P})$ where…

Functional Analysis · Mathematics 2016-11-21 Keita Owari

We consider variational problems that model the bending behavior of curves that are constrained to belong to given hypersurfaces. Finite element discretizations of corresponding functionals are justified rigorously via Gamma-convergence.…

Numerical Analysis · Mathematics 2020-04-24 Sören Bartels

We find uniform with respect to parameter $p \ (1\leq p\leq\infty)$ upper estimations of best approximations by trigonometric polynomials of classes $C^{\psi}_{\beta,p}$ of periodic functions generated by sequences $\psi(k)$, that decrease…

Classical Analysis and ODEs · Mathematics 2014-10-16 A. S. Serdyuk , T. A. Stepaniuk

Many interesting functions arising in applications map into Riemannian manifolds. We present an algorithm, using the manifold exponential and logarithm, for approximating such functions. Our approach extends approximation techniques for…

Numerical Analysis · Mathematics 2026-01-27 Simon Jacobsson , Raf Vandebril , Joeri van der Veken , Nick Vannieuwenhoven

In this paper, we provide the maximal boundedness range (up to end-points) for the Bilinear Hilbert-Carleson operator along curves in the (purely) non-zero curvature setting. More precisely, we show that the operator $$…

Classical Analysis and ODEs · Mathematics 2025-07-08 Árpád Bényi , Bingyang Hu , Victor Lie

We are concerned with the following semi-linear polyharmonic equation with integral constraint \begin{align} \left\{\begin{array}{rl} &(-\Delta)^pu=u^\gamma_+ ~~ \mbox{ in }{\mathbb{R}^n},\\ \nonumber…

Analysis of PDEs · Mathematics 2022-08-08 Zhuoran Du , Zhenping Feng , Yuan Li

We consider the $L^p$ mapping properties of maximal averages associated to families of curves, and thickened curves, in the plane. These include the (planar) Kakeya maximal function, the circular maximal functions of Wolff and Bourgain, and…

Classical Analysis and ODEs · Mathematics 2025-10-09 Joshua Zahl

Let S be a subset of the unit disk, and let F(s) denote the class of completely multiplicative functions f such that f(p) is in S for all primes p. The authors' main concern is which numbers arise as mean-values of functions in F(s). More…

Number Theory · Mathematics 2016-09-07 Andrew Granville , K. Soundararajan

We consider the class $\Sigma(p)$ of univalent meromorphic functions $f$ on $\ID$ having simple pole at $z=p\in[0,1)$ with residue 1. Let $\Sigma_k(p)$ be the class of functions in $\Sigma(p)$ which have $k$-quasiconformal extension to the…

Complex Variables · Mathematics 2017-05-11 Bappaditya Bhowmik , Goutam Satpati

Let f be a germ of an analytic function at infinity that can be analytically continued along any path in the complex plane deprived of a finite set of points, f \in\mathcal{A}(\bar{\C} \setminus A), \sharp A <\infty. J. Nuttall has put…

Classical Analysis and ODEs · Mathematics 2016-01-12 Alexander I. Aptekarev , Maxim L. Yattselev

We develop almost-orthogonality principles for maximal functions associated with averages over line segments and directional singular integrals. Using them, we obtain sharp $L^2$-bounds for these maximal functions when the underlying…

Classical Analysis and ODEs · Mathematics 2025-10-13 Jongchon Kim

In this talk, I will discuss the use of harmonic functions to study the geometry and topology of complete manifolds. In my previous joint work with Luen-fai Tam, we discovered that the number of infinities of a complete manifold can be…

Differential Geometry · Mathematics 2007-05-23 Peter Li

We prove that the maximal operator associated with variable homogeneous planar curves $(t, u t^{\alpha})_{t\in \mathbb{R}}$, $\alpha\not=1$ positive, is bounded on $L^p(\mathbb{R}^2)$ for each $p>1$, under the assumption that…

Classical Analysis and ODEs · Mathematics 2017-10-31 Shaoming Guo , Jonathan Hickman , Victor Lie , Joris Roos

Many popular learning algorithms (E.g. Regression, Fourier-Transform based algorithms, Kernel SVM and Kernel ridge regression) operate by reducing the problem to a convex optimization problem over a vector space of functions. These methods…

Machine Learning · Computer Science 2014-05-13 Amit Daniely , Nati Linial , Shai Shalev-Shwartz
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