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Despite its important applications in Machine Learning, min-max optimization of nonconvex-nonconcave objectives remains elusive. Not only are there no known first-order methods converging even to approximate local min-max points, but the…

Computational Complexity · Computer Science 2020-09-22 Constantinos Daskalakis , Stratis Skoulakis , Manolis Zampetakis

We study the nonlinear Schr\"odinger equation for the $s-$fractional $p-$Laplacian strongly coupled with the Poisson equation in dimension two and with $p=\frac2s$, which is the limiting case for the embedding of the fractional Sobolev…

Analysis of PDEs · Mathematics 2025-07-23 Daniele Cassani , Zhisu Liu , Giulio Romani

A set of piecewise linear functions, called polylines, $P_1,\ldots,P_L$ each with at most $n$ vertices can be simplified into a polyline $M$ with $k$ vertices, such that the Fr\'echet distances $\epsilon_1,\ldots,\epsilon_L$ to each of…

Computational Geometry · Computer Science 2021-08-30 Sepideh Aghamolaei , Mohammad Ghodsi

Quasi-Monte Carlo (QMC) quadrature rules using higher order digital nets and sequences have been shown to achieve the almost optimal rate of convergence of the worst-case error in Sobolev spaces of arbitrary fixed smoothness $\alpha\in…

Numerical Analysis · Mathematics 2019-12-09 Takashi Goda , Kosuke Suzuki , Takehito Yoshiki

We study Tikhonov regularization for possibly nonlinear inverse problems with weighted $\ell^1$-penalization. The forward operator, mapping from a sequence space to an arbitrary Banach space, typically an $L^2$-space, is assumed to satisfy…

Numerical Analysis · Mathematics 2021-10-19 Philip Miller , Thorsten Hohage

Let $(M,g)$ be a closed Riemannian manifold of dimension $n$, and $k\geq 1$ an integer such that $n>2k$. We show that there exists $B_0>0$ such that for all $u \in H^{k}(M)$, \[\|u\|_{L^{2^\sharp}(M)}^2 \leq K_0^2 \int_M |\Delta_g^{k/2}…

Analysis of PDEs · Mathematics 2025-06-30 Lorenzo Carletti

$\renewcommand{\Re}{\mathbb{R}}\newcommand{\eps}{{\varepsilon}}\newcommand{\poly}{\mathrm{poly}} $In this paper, we study the problem of $L_1$-fitting a shape to a set of $n$ points in $\Re^d$ (where $d$ is a fixed constant), where the…

Computational Geometry · Computer Science 2026-01-21 Sariel Har-Peled

We study the problem of minimizing a nonnegative separable concave function over a compact feasible set. We approximate this problem to within a factor of 1+epsilon by a piecewise-linear minimization problem over the same feasible set. Our…

Optimization and Control · Mathematics 2012-01-17 Thomas L. Magnanti , Dan Stratila

We revisit the moving least squares (MLS) approximation scheme on the sphere $\mathbb S^{d-1} \subset \mathbb R^d$, where $d>1$. It is well known that using the spherical harmonics up to degree $L \in \mathbb N$ as ansatz space yields for…

Numerical Analysis · Mathematics 2024-10-25 Ralf Hielscher , Tim Pöschl

We consider the {\em Shaped Partition Problem} of partitioning $n$ given vectors in real $k$-space into $p$ parts so as to maximize an arbitrary objective function which is convex on the sum of vectors in each part, subject to arbitrary…

Combinatorics · Mathematics 2016-09-07 Frank K. Hwang , Shmuel Onn , Uriel G. Rothblum

We construct Monte Carlo methods for the $L^2$-approximation in Hilbert spaces of multivariate functions sampling no more than $n$ function values of the target function. Their errors catch up with the rate of convergence and the…

Numerical Analysis · Mathematics 2018-03-16 David Krieg

In this paper, we investigate the approximation problem for functions in Gaussian Sobolev spaces $W^s_p(\mathbb{R}^d, \gamma)$ of smoothness $s > 0$, where the approximation error is measured in the Gaussian Lebesgue space…

Functional Analysis · Mathematics 2026-04-21 Van Kien Nguyen

The best column approximation in the Frobenius norm with $r$ columns has an error at most $\sqrt{r+1}$ times larger than the truncated singular value decomposition. Reaching this bound in practice involves either expensive random volume…

Numerical Analysis · Mathematics 2023-11-08 Alexander Osinsky

We propose a near-optimal method for highly smooth convex optimization. More precisely, in the oracle model where one obtains the $p^{th}$ order Taylor expansion of a function at the query point, we propose a method with rate of convergence…

Optimization and Control · Mathematics 2019-06-25 Sébastien Bubeck , Qijia Jiang , Yin Tat Lee , Yuanzhi Li , Aaron Sidford

The subspace approximation problem Subspace($k$,$p$) asks for a $k$-dimensional linear subspace that fits a given set of points optimally, where the error for fitting is a generalization of the least squares fit and uses the $\ell_{p}$ norm…

Data Structures and Algorithms · Computer Science 2011-01-04 Amit Deshpande , Kasturi Varadarajan , Madhur Tulsiani , Nisheeth K. Vishnoi

Given an arbitrary function in H(div), we show that the error attained by the global-best approximation by H(div)-conforming piecewise polynomial Raviart-Thomas-N\'ed\'elec elements under additional constraints on the divergence and normal…

Numerical Analysis · Mathematics 2020-05-20 Alexandre Ern , Thirupathi Gudi , Iain Smears , Martin Vohralík

Finding approximate stationary points, i.e., points where the gradient is approximately zero, of non-convex but smooth objective functions $f$ over unrestricted $d$-dimensional domains is one of the most fundamental problems in classical…

Optimization and Control · Mathematics 2024-09-13 Alexandros Hollender , Manolis Zampetakis

We show that for large enough $n$, the number of non-isomorphic pseudoline arrangements of order $n$ is greater than $2^{c\cdot n^2}$ for some constant $c > 0.2604$, improving the previous best bound of $c>0.2083$ by Dumitrescu and Mandal…

Computational Geometry · Computer Science 2024-02-22 Justin Dallant

We introduce and study the problem of consistent low-rank approximation, in which rows of an input matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ arrive sequentially and the goal is to provide a sequence of subspaces that well-approximate the…

Data Structures and Algorithms · Computer Science 2026-03-03 David P. Woodruff , Samson Zhou

We study approximation algorithms for the following geometric version of the maximum coverage problem: Let $\mathcal{P}$ be a set of $n$ weighted points in the plane. Let $D$ represent a planar object, such as a rectangle, or a disk. We…

Computational Geometry · Computer Science 2017-12-08 Kai Jin , Jian Li , Haitao Wang , Bowei Zhang , Ningye Zhang
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