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Linear algebraic expressions are the essence of many computationally intensive problems, including scientific simulations and machine learning applications. However, translating high-level formulations of these expressions to efficient…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-03-22 Dániel Berényi , András Leitereg , Gábor Lehel

We derive a formula for the entries in the change-of-basis matrix between Young's seminormal and natural representations of the symmetric group. These entries are determined as sums over weighted paths in the weak Bruhat graph on standard…

Representation Theory · Mathematics 2020-12-08 Sam Armon , Tom Halverson

We consider directed graphs where each edge is labeled with an integer weight and study the fundamental algorithmic question of computing the value of a cycle with minimum mean weight. Our contributions are twofold: (1) First we show that…

Data Structures and Algorithms · Computer Science 2013-07-18 Krishnendu Chatterjee , Monika Henzinger , Sebastian Krinninger , Veronika Loitzenbauer

For zero-error function computation over directed acyclic networks, existing upper and lower bounds on the computation capacity are known to be loose. In this work we consider the problem of computing the arithmetic sum over a specific…

Information Theory · Computer Science 2016-01-28 Ardhendu Tripathy , Aditya Ramamoorthy

Can transformers generalize efficiently on problems that require dealing with examples with different levels of difficulty? We introduce a new task tailored to assess generalization over different complexities and present results that…

Matrix multiplication is a fundamental task in almost all computational fields, including machine learning and optimization, computer graphics, signal processing, and graph algorithms (static and dynamic). Twin-width is a natural complexity…

Data Structures and Algorithms · Computer Science 2026-02-24 László Kozma , Michal Opler

The singular values of convolutional mappings encode interesting spectral properties, which can be used, e.g., to improve generalization and robustness of convolutional neural networks as well as to facilitate model compression. However,…

Machine Learning · Computer Science 2025-06-09 Antonia van Betteray , Matthias Rottmann , Karsten Kahl

Recombining trinomial trees are a workhorse for modeling discrete-event systems in option pricing, logistics, and feedback control. Because each node stores a state-dependent quantity, a depth-$D$ tree naively yields $\mathcal{O}(3^{D})$…

Data Structures and Algorithms · Computer Science 2025-10-06 Ethan Torres , Ramavarapu Sreenivas , Richard Sowers

A polynomial transform is the multiplication of an input vector $x\in\C^n$ by a matrix $\PT_{b,\alpha}\in\C^{n\times n},$ whose $(k,\ell)$-th element is defined as $p_\ell(\alpha_k)$ for polynomials $p_\ell(x)\in\C[x]$ from a list…

Information Theory · Computer Science 2011-07-14 Aliaksei Sandryhaila , Jelena Kovacevic , Markus Pueschel

We revisit the Subset Sum problem over the finite cyclic group $\mathbb{Z}_m$ for some given integer $m$. A series of recent works has provided near-optimal algorithms for this problem under the Strong Exponential Time Hypothesis. Koiliaris…

Data Structures and Algorithms · Computer Science 2020-11-02 Kyriakos Axiotis , Arturs Backurs , Karl Bringmann , Ce Jin , Vasileios Nakos , Christos Tzamos , Hongxun Wu

It is well known that, using fast algorithms for polynomial multiplication and division, evaluation of a polynomial $F \in \mathbb{C}[x]$ of degree $n$ at $n$ complex-valued points can be done with $\tilde{O}(n)$ exact field operations in…

Numerical Analysis · Computer Science 2016-05-30 Alexander Kobel , Michael Sagraloff

The author derives new family of series representations for the values of the Riemann Zeta function $\zeta(s)$ at positive odd integers. For $n\in\mathbb{N}$, each of these series representing $\zeta(2n+1)$ converges remarkably rapidly with…

Number Theory · Mathematics 2018-06-22 Guang-Qing Bi

Matrix multiplication (hereafter we use the acronym MM) is among the most fundamental operations of modern computations. The efficiency of its performance depends on various factors, in particular vectorization, data movement and arithmetic…

Data Structures and Algorithms · Computer Science 2015-02-09 Victor Y. Pan

Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions. All of these transforms can be represented by dense…

Machine Learning · Computer Science 2021-01-01 Tri Dao , Albert Gu , Matthew Eichhorn , Atri Rudra , Christopher Ré

Tree-width and path-width are well-known graph parameters. Many NP-hard graph problems allow polynomial-time solutions, when restricted to graphs of bounded tree-width or bounded path-width. In this work, we study the behavior of tree-width…

Data Structures and Algorithms · Computer Science 2024-06-14 Frank Gurski , Robin Weishaupt

This paper continues the study of combinatorial properties of binary functions --- that is, functions $f:2^E\rightarrow\mathbb{C}$ such that $f(\emptyset)=1$, where $E$ is a finite set. Binary functions have previously been shown to admit…

Combinatorics · Mathematics 2017-08-22 G. E. Farr

An $n$-by-$n$ ($n\ge 3$) weighted shift matrix $A$ is one of the form $$[{array}{cccc}0 & a_1 & & & 0 & \ddots & & & \ddots & a_{n-1} a_n & & & 0{array}],$$ where the $a_j$'s, called the weights of $A$, are complex numbers. Assume that all…

Functional Analysis · Mathematics 2013-10-22 Hwa-Long Gau , Ming-Cheng Tsai , Han-Chun Wang

We first study birational mappings generated by the composition of the matrix inversion and of a permutation of the entries of $ 3 \times 3 $ matrices. We introduce a semi-numerical analysis which enables to compute the Arnold complexities…

chao-dyn · Physics 2019-08-17 N. Abarenkova , J-. Ch. Anglès d'Auriac , S. Boukraa , J. -M. Maillard

Submodular functions are a fundamental object of study in combinatorial optimization, economics, machine learning, etc. and exhibit a rich combinatorial structure. Many subclasses of submodular functions have also been well studied and…

Data Structures and Algorithms · Computer Science 2013-04-19 Nikhil R. Devanur , Shaddin Dughmi , Roy Schwartz , Ankit Sharma , Mohit Singh

The modular decomposition of a graph is a canonical representation of its modules. Algorithms for computing the modular decomposition of directed and undirected graphs differ significantly, with the undirected case being simpler, and…

Discrete Mathematics · Computer Science 2017-10-13 Henning Koehler