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We study the extremal Forrelation problem, where, provided with oracle access to Boolean functions $f$ and $g$ promised to satisfy either $\textrm{forr}(f,g)=1$ or $\textrm{forr}(f,g)=-1$, one must determine (with high probability) which of…

Computational Complexity · Computer Science 2026-02-10 Clément L. Canonne , Kenny Chen , Julián Mestre

We propose practical algorithms for entrywise $\ell_p$-norm low-rank approximation, for $p = 1$ or $p = \infty$. The proposed framework, which is non-convex and gradient-based, is easy to implement and typically attains better…

Machine Learning · Computer Science 2018-05-25 Anastasios Kyrillidis

In view of the minimization of a function which is the sum of a differentiable function $f$ and a convex function $g$ we introduce descent methods which can be viewed as produced by inexact auxiliary problem principleor inexact variable…

Optimization and Control · Mathematics 2016-09-13 Jean-Philippe Chancelier

The problem of minimizing convex functionals of probability distributions is solved under the assumption that the density of every distribution is bounded from above and below. A system of sufficient and necessary first-order optimality…

Information Theory · Computer Science 2018-12-05 Michael Fauss , Abdelhak M. Zoubir

Let us assume that $f$ is a continuous function defined on the unit ball of $\mathbb R^d$, of the form $f(x) = g (A x)$, where $A$ is a $k \times d$ matrix and $g$ is a function of $k$ variables for $k \ll d$. We are given a budget $m \in…

Numerical Analysis · Mathematics 2012-01-18 Massimo Fornasier , Karin Schnass , Jan Vybiral

We construct new Delone sets associated with badly approximable numbers which are expected to have rotationally invariant diffraction. We optimize the discrepancy of corresponding tile orientations by investigating the linear equation…

Number Theory · Mathematics 2025-03-18 Shigeki Akiyama , Emily R. Korfanty , Yanli Xu

Let $A$ and $B$ be two point sets in the plane of sizes $r$ and $n$ respectively (assume $r \leq n$), and let $k$ be a parameter. A matching between $A$ and $B$ is a family of pairs in $A \times B$ so that any point of $A \cup B$ appears in…

Data Structures and Algorithms · Computer Science 2019-03-25 Pankaj K. Agarwal , Hsien-Chih Chang , Allen Xiao

We consider domination analysis of approximation algorithms for the bipartite boolean quadratic programming problem (BBQP) with m+n variables. A closed form formula is developed to compute the average objective function value A of all…

Optimization and Control · Mathematics 2014-12-30 Abraham P. Punnen , Piyashat Sripratak , Daniel Karapetyan

We consider variants of the classic Multiway Cut problem. Multiway Cut asks to partition a graph $G$ into $k$ parts so as to separate $k$ given terminals. Recently, Chandrasekaran and Wang (ESA 2021) introduced $\ell_p$-norm Multiway, a…

Data Structures and Algorithms · Computer Science 2023-08-17 Charlie Carlson , Jafar Jafarov , Konstantin Makarychev , Yury Makarychev , Liren Shan

This paper extends the algorithm schemes proposed in \cite{Nesterov2007a} and \cite{Nesterov2007b} to the minimization of the sum of a composite objective function and a convex function. Two proximal point-type schemes are provided and…

Optimization and Control · Mathematics 2011-05-03 Quoc Tran Dinh , Moritz Diehl

The Boolean matrix factorization problem consists in approximating a matrix by the Boolean product of two smaller Boolean matrices. To obtain optimal solutions when the matrices to be factorized are small, we propose SAT and MaxSAT…

Machine Learning · Computer Science 2021-06-21 Florent Avellaneda , Roger Villemaire

We consider minimizing a function consisting of a quadratic term and a proximable term which is possibly nonconvex and nonsmooth. This problem is also known as scaled proximal operator. Despite its simple form, existing methods suffer from…

Optimization and Control · Mathematics 2024-03-01 Yiming Zhou , Wei Dai

We study the complexity of approximate representation and learning of submodular functions over the uniform distribution on the Boolean hypercube $\{0,1\}^n$. Our main result is the following structural theorem: any submodular function is…

Machine Learning · Computer Science 2013-04-03 Vitaly Feldman , Pravesh Kothari , Jan Vondrak

We consider a minimization problem of the form $P(\varphi, g, h):$ $$\min\left\{f(x):= \varphi(x) + g(x) - h(x) \colon x \in \mathbb{R}^n\right\},$$ where $\varphi$ is a differentiable function and $g,$ $h$ are convex functions, and…

Optimization and Control · Mathematics 2026-03-11 Shuang Wu , Bui Van Dinh , Liguo Jiao , Do Sang Kim , Wensheng Zhu

The degrees of polynomials representing or approximating Boolean functions are a prominent tool in various branches of complexity theory. Sherstov recently characterized the minimal degree deg_{\eps}(f) among all polynomials (over the…

Quantum Physics · Physics 2008-02-15 Ronald de Wolf

The $\epsilon$-approximate degree $deg_\epsilon(f)$ of a Boolean function $f$ is the least degree of a real-valued polynomial that approximates $f$ pointwise to error $\epsilon$. The approximate degree of $f$ is at least $k$ iff there…

Computational Complexity · Computer Science 2019-06-04 Andrej Bogdanov , Nikhil S. Mande , Justin Thaler , Christopher Williamson

The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function value of instances from the underlying distribution of…

Machine Learning · Computer Science 2025-11-05 Nimrod Megiddo , Segev Wasserkrug , Orit Davidovich , Shimrit Shtern

Nisan and Szegedy (CC 1994) showed that any Boolean function $f:\{0,1\}^n\rightarrow \{0,1\}$ that depends on all its input variables, when represented as a real-valued multivariate polynomial $P(x_1,\ldots,x_n)$, has degree at least $\log…

Computational Complexity · Computer Science 2021-07-08 Srikanth Srinivasan , S. Venkitesh

We obtain the result of approximating \( f \) in the \( H^1(\mathbb{R}) \) norm using partial Hausdorff integrals. Specifically, by leveraging the homogeneous multiplier theory of \( H^1(\mathbb{R}) \) and the \( K \) functional theory, one…

Classical Analysis and ODEs · Mathematics 2025-12-04 Zifei Yu , Baode Li

We investigate the approximability of several classes of real-valued functions by functions of a small number of variables ({\em juntas}). Our main results are tight bounds on the number of variables required to approximate a function…

Data Structures and Algorithms · Computer Science 2015-03-31 Vitaly Feldman , Jan Vondrak
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