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We consider the problem of minimizing a convex function over the intersection of finitely many simple sets which are easy to project onto. This is an important problem arising in various domains such as machine learning. The main difficulty…

Optimization and Control · Mathematics 2017-10-19 Achintya Kundu , Francis Bach , Chiranjib Bhattacharyya

We focus on Legendrian submanifolds of the space of one-jets of functions, $J^1(\mathbb{R}^n,\mathbb{R})$. We are interested in processes - operations - that build new Legendrian submanifolds from old ones. We introduce in particular two…

Symplectic Geometry · Mathematics 2017-06-02 M. Limouzineau

Subset Sum is a classical optimization problem taught to undergraduates as an example of an NP-hard problem, which is amenable to dynamic programming, yielding polynomial running time if the input numbers are relatively small. Formally,…

Data Structures and Algorithms · Computer Science 2018-07-24 Konstantinos Koiliaris , Chao Xu

We establish a broad generalization of Whitney's broken circuit theorem on the chromatic polynomial of a graph to sums of type $\sum_{A\subseteq S} f(A)$ where $S$ is a finite set and $f$ is a mapping from the power set of $S$ into an…

Combinatorics · Mathematics 2025-12-03 Klaus Dohmen , Martin Trinks

The affine Weyl groups with their corresponding four types of orbit functions are considered. Two independent admissible shifts, which preserve the symmetries of the weight and the dual weight lattices, are classified. Finite subsets of the…

Mathematical Physics · Physics 2014-11-17 Tomasz Czyżycki , Jiří Hrivnák

We prove that the inverse of a positive-definite matrix can be approximated by a weighted-sum of a small number of matrix exponentials. Combining this with a previous result [OSV12], we establish an equivalence between matrix inversion and…

Data Structures and Algorithms · Computer Science 2016-08-23 Sushant Sachdeva , Nisheeth K. Vishnoi

We discuss some aspects of approximating functions on high-dimensional data sets with additive functions or ANOVA decompositions, that is, sums of functions depending on fewer variables each. It is seen that under appropriate smoothness…

Data Structures and Algorithms · Computer Science 2009-11-17 Markus Hegland , Vladimir Pestov

The best method for computing the adjoint matrix of an order $n$ matrix in an arbitrary commutative ring requires $O(n^{\beta+1/3}\log n \log \log n)$ operations, provided the complexity of the algorithm for multiplying two matrices is…

Symbolic Computation · Computer Science 2017-11-28 Alkiviadis Akritas , Gennadi Malaschonok

Symmetric tensor decomposition is an important problem with applications in several areas for example signal processing, statistics, data analysis and computational neuroscience. It is equivalent to Waring's problem for homogeneous…

Symbolic Computation · Computer Science 2019-09-12 Matías Bender , Jean-Charles Faugère , Ludovic Perret , Elias Tsigaridas

For an analytic family P_s of polynomials in n variables (depending on a complex number s, and defined in a neighborhood of s = 0), there is defined a monodromy transformation h of the zero level set V_s= {P_s=0} for s different from 0,…

Algebraic Geometry · Mathematics 2024-07-22 S. M. Gusein-Zade , D. Siersma

We relate the computational complexity of finite strings to universal representations of their underlying symmetries. First, Boolean functions are classified using the universal covering topologies of the circuits which enumerate them. A…

Information Theory · Computer Science 2011-09-20 John Scoville

Let $\sigma_a^{(N)}(n)=\sum_{d^{N}|n}d^a$. An explicit transformation is obtained for the generalized Lambert series $\sum_{n=1}^{\infty}\sigma_{a}^{(N)}(n)e^{-ny}$ for Re$(a)>-1$ using the recently established Vorono\"i summation formula…

Number Theory · Mathematics 2023-04-13 Soumyarup Banerjee , Atul Dixit , Shivajee Gupta

We revisit the fundamental Boolean Matrix Multiplication (BMM) problem. With the invention of algebraic fast matrix multiplication over 50 years ago, it also became known that BMM can be solved in truly subcubic $O(n^\omega)$ time, where…

Data Structures and Algorithms · Computer Science 2024-05-28 Amir Abboud , Nick Fischer , Zander Kelley , Shachar Lovett , Raghu Meka

The Mertens function, $M(x) := \sum_{n \leq x} \mu(n)$, is defined as the summatory function of the classical M\"obius function. The Dirichlet inverse function $g(n) := (\omega+1)^{-1}(n)$ is defined in terms of the shifted strongly…

Number Theory · Mathematics 2022-07-19 Maxie Dion Schmidt

The most studied linear algebraic operation, matrix multiplication, has surprisingly fast $O(n^\omega)$ time algorithms for $\omega<2.373$. On the other hand, the $(\min,+)$ matrix product which is at the heart of many fundamental graph…

Computational Complexity · Computer Science 2020-10-01 Andrea Lincoln , Adam Polak , Virginia Vassilevska Williams

Given n positive integers, the Modular Subset Sum problem asks if a subset adds up to a given target t modulo a given integer m. This is a natural generalization of the Subset Sum problem (where m=+\infty) with ties to additive…

Data Structures and Algorithms · Computer Science 2018-07-16 Kyriakos Axiotis , Arturs Backurs , Christos Tzamos

The complexity of software implementations of MDS erasure codes mainly depends on the efficiency of the finite field operations implementation. In this paper, we propose a method to reduce the complexity of the finite field multiplication…

Information Theory · Computer Science 2017-06-14 Jonathan Detchart , Jérôme Lacan

We investigate sumset decompositions of quite general sets with restricted prime factors. We manage to handle certain sets, such as the smooth numbers, even though they have little sieve amenability, and conclude that these sets cannot be…

Number Theory · Mathematics 2013-09-04 Christian Elsholtz , Adam J. Harper

We consider active, semi-supervised learning in an offline transductive setting. We show that a previously proposed error bound for active learning on undirected weighted graphs can be generalized by replacing graph cut with an arbitrary…

Machine Learning · Computer Science 2012-02-20 Andrew Guillory , Jeff A. Bilmes

We consider the numerical solution of the discrete multi-marginal optimal transport (MOT) by means of the Sinkhorn algorithm. In general, the Sinkhorn algorithm suffers from the curse of dimensionality with respect to the number of…

Optimization and Control · Mathematics 2023-02-22 Fatima Antarou Ba , Michael Quellmalz
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